masthesis_futureofmicroaerialvehicles

Running head: MICRO AIR VEHICLES: THE FUTURE OF UNMANNED ATMOSPHERIC AND SPACE FLIGHT 1

ASCI 691 Graduate Capstone

Micro Air Vehicles: The Future of Unmanned Atmospheric and Space Flight

Astrid Thundercliffe

Embry-Riddle Aeronautical University

ASCI 691 Graduate Capstone

Submitted to the Worldwide Campus

In Partial Fulfillment of the Requirements of the Degree of

Master of Aeronautical Science

2 March 2014

MICRO AIR VEHICLES: THE FUTURE OF UNMANNED ATMOSPHERIC AND SPACE FLIGHT 2

Abstract

The unmanned flight capabilities of the emerging class of micro air vehicle (MAV) will be

examined using existing operational data on traditional unmanned air vehicles (UAVs) as well as

experimental aerodynamics data for next generation low Reynolds number aircraft. The benefits

and limitations of MAV use for remote sensing and exploration of atmospheric and spaceflight

will be discussed. The mission and design requirements of MAVs for future terrestrial

surveillance and Mars exploration will be analyzed using present Department of Defense and

National Air and Space Administration (NASA) program initiatives. Human factors associated

with the command and control of MAVs will be explored, and recommendations for MAV

control systems will be made. The integration of unmanned aerial vehicles, including MAVs, in

the civil airspace will be discussed according to recent Federal Aviation Administration (FAA)

legislation. National and international space policy will be examined for concerns about MAV

use and exploration of Mars.

Keywords: micro air vehicle, unmanned air vehicle, low Reynolds number, remote

sensing, atmospheric flight, spaceflight, human factors, control systems, Federal Aviation

Administration, Department of Defense, National Air and Space Administration, civil airspace,

space policy


Proposal


The challenges of manned flight have dominated the aeronautical field for the majority of

the 20th century. The successful harnessing of manned atmospheric and spaceflight physics has

given rise to ever greater flight capabilities including the recent emergence of unmanned air

vehicles (UAVs). Unmanned platforms of the 21st century are being driven by the requirements

of civilian, government, and military objectives alike and have been made possible by the

technological advances yielded by a century of manned flight (Pines & Bohorquez, 2006). The

latest innovations in UAVs have given rise to a new class of aircraft called a micro air vehicle

(MAV) which is defined to be less than 6 inches in any given dimension with a gross takeoff

weight of 200 g or less (Pines & Bohorquez, 2006). This new class of UAV presents even

greater options for surveillance and reconnaissance operations in a wide variety of environments

which have been previously been inaccessible or potentially hostile to both manned and

unmanned flight platforms from enclosed urban environments to the low oxygen atmospheric

environment of Mars.

This proposed project investigates the capabilities and design considerations of current

MAV flight platforms to assess their effectiveness at meeting the expanding requirements of

UAV flight for both terrestrial and space applications. Specifically, Earth-based remote sensing

mission requirements and the flight requirements for successful MAV operations on Mars will be

determined and current MAV platforms will be investigated with a focus on their ability to meet

one or both mission profiles. The flight efficiency and performance characteristics of current

MAV platforms will be examined using an ANOVA statistical analysis and compared to the

flight efficiency and performance characteristics of existing mission capable large-scale UAV


platforms in order to understand the effectiveness of the emerging flight characteristics of recent

MAV designs. Next generation aerodynamics research and MAV prototypes using low

Reynolds number flight and biokinetic flight mechanics will be evaluated for their future ability

to fulfill the terrestrial mission and Mars mission requirements.

Program Outcomes

PO#1

Students will be able to apply the fundamentals of air transportation as part of a global,

multimodal transportation system, including the technological, social, environmental, and

political aspects of the system to examine, compare, analyze and recommend conclusion.

Micro air vehicles (MAVs) are an emerging technology which will have a variety

of applications particularly for low Reynolds number and low oxygen flight

conditions (Michelson & Naqvi, 2003). The future role for MAVs will be

explored as part of the air transportation system on Earth and for use on future

Mars missions.

The technological design aspects required for consistent flight of MAVs in the

Mars and Earth environments will be examined with respect to flight efficiency

and performance characteristics of current MAV platforms and prototypes. As of

2012, the Federal Aviation Agency (FAA) has been tasked with the integration of

unmanned aircraft into the United States’ civil airspace (Federal Aviation

Administration, 2013).

The social impacts of widespread use of MAVs for urban surveillance focus

particularly on privacy concerns of governments and civilians alike and will be

analyzed with respect to the impacts present in known traditional UAV aircraft.


The environmental concerns of unmanned aircraft integration will be analyzed

using existing studies on UAV impacts, and space-based environmental aspects

will be addressed using current international space legislation.

The political aspects of use of MAVs for terrestrial surveillance and

reconnaissance missions as well as for future Mars exploration will be discussed

by means of relevant existing unmanned aircraft and space legislation.

PO#2

The student will be able to identify and apply appropriate statistical analysis, to include

techniques in data collection, review, critique, interpretation and inference in the aviation and

aerospace industry.

The flight efficiency and performance characteristics of current micro air vehicle

(MAV) platforms will be analyzed to determine overall MAV suitability for the

expanding mission requirements of atmospheric and spaceflight unmanned

aircraft using an ANOVA statistical analysis.

Terrestrial mission requirements will be identified using current Department of

Defense and Defense Advanced Research Projects Agency (DARPA) unmanned

aircraft development reports. National and international developmental

performance requirements will be reviewed. Mission requirements for a future

Mars mission will be identified using NASA Innovative Advanced Concepts

reports and current NASA Mars mission objective reports and MAV suitability

for the Mars environment will be assessed.


A comparison of flight efficiency performance characteristics of MAVs will be

conducted using existing flight data collected from operational MAV and UAV

aircraft.

MAV suitability for future Department of Defense mission requirements and

MAV flight capability for the Mars environment will be assessed using

interpretation from the results of an ANOVA statistical analysis.

PO#3

The student will be able across all subjects to use the fundamentals of human factors in all

aspects of the aviation and aerospace industry, including unsafe acts, attitudes, errors, human

behavior, and human limitations as they relate to the aviators adaption to the aviation

environment to reach conclusions.

The human factors of piloting and maintaining micro air vehicles (MAVs) will

be discussed using known data from traditional UAVs, and the considerations of

smaller size constraints in MAVs will be assessed with respect to human

limitations inherent in command and control of MAV aircraft.

Federal Aviation Administration (FAA) and Department of Defense accident

studies on existing UAV operations will be analyzed to determine the strengths

and weakness of unmanned aircraft relating to unsafe acts.

Hazardous attitudes affecting the decision making processes of existing UAV

crews will be assessed and related to similar concerns for future MAV operations.

Existing and experimental MAV and UAV autonomous, semi-autonomous, and

non-autonomous control systems will be investigated for their design


effectiveness in relation to reducing human error and enhancing remote piloting

options for MAV platforms.

Reports on human behavior from the Department of Defense and Federal

Aviation Administration (FAA) will be assessed with respect to the flight

operations and crew selection of existing UAVs and assessed for application in

future MAV missions.

PO#4

The student will be able to develop and/or apply current aviation and industry related research

methods, including problem identification, hypothesis formulation, and interpretation of findings

to present as solutions in the investigation of an aviation / aerospace related topic.

Statistical analysis will be conducted using an ANOVA analysis on micro air

vehicle (MAV) flight design characteristics to accept or reject the hypothesis that

there is a statistically significant difference between MAV flight efficiency and

performance parameters and the parameters of currently operational UAVs.

Current UAV aircraft have been technologically limited to operations in

environments and aerodynamic conditions similar to those of traditional manned

aircraft (Pines & Bohorquez, 2006). A statistical difference between MAV and

UAV flight design characteristics will identify the existence of a new class of

UAVs that is capable of operating in currently unattainable flight conditions.

Interpretation of flight characteristics data will be performed with respect to

suitability of MAVs to perform the mission requirements for operations in

terrestrial remote sensing and future Mars exploration. Design data for existing


MAVs and currently operational UAVs will be acquired from flight specification

data for existing flight platforms.

PO#5: Aeronautics

The student will investigate, compare, contrast, analyze and form conclusions to current

aviation, aerospace, and industry related topics in aeronautics, including advanced

aerodynamics, advanced aircraft performance, simulation systems, crew resource management,

advanced meteorology, rotorcraft operations and advanced aircraft/spacecraft systems.

The design characteristics of current micro air vehicle (MAV) flight platforms

will be explored through analysis using advanced aircraft performance flight

data.

Advanced aerodynamics research in the field of low Reynolds number flight

capabilities for MAVs will be examined for the enhancement of MAV technology

for future unmanned missions on Earth and Mars.

The meteorological effects present in terrestrial hazardous environments and the

low oxygen environments of Mars will be analyzed to determine flight

requirements for the design of MAVs in each respective environment.

Rotorcraft operations of flapping and rotor MAV designs will be discussed, and

rotor design benefits and limitations will be investigated for existing and future

MAV aircraft.

Emerging materials technology and understanding of low Reynolds number

aerodynamics used for existing and future MAV designs with be determined with

respect to known advanced aircraft/spacecraft systems.


This student did not take classes in simulation systems or crew resource

management.

PO#11: Space Studies

The student will investigate, compare, contrast, analyze and form conclusions to current

aviation, aerospace, and industry related topics in space studies, including earth observation

and remote sensing, mission and launch operations, habitation and life support systems, and

applications in space commerce, defense, and exploration.

Earth observation using MAVs particularly for surveillance and reconnaissance

operations will be explored using known operational UAV data and Department

of Defense reports for future MAV mission requirements.

Remote sensing sensor capabilities will be explored given current size and power

constraints of MAV designs for terrestrial and spaceflight purposes.

MAV integration for a manned future mission to Mars will be explored for use

during space mission operations. Requirements for launch to Mars will be

determined based on current NASA Innovative Advanced Concepts reports and

current NASA Mars mission objective reports.

Maintenance and support for MAVs during a manned Mars mission and possible

uses for exploration and manned habitation of Mars will be discussed.

The uses of MAV for space commerce, defense, and exploration will be

analyzed using current information on MAV prototypes as well as DoD and

NASA reports on MAV capabilities for hostile environments of space and

terrestrial defense.



Introduction

Micro Aerial Vehicles have been in development for more than a decade spurred by

recent technological advancements in aerodynamics and materials science (Carey, 2007).

Unmanned Aerial Vehicles (UAVs) have been in operation since the 1950s during World War I

(Mueller, 2007). Functional MAVs the size of a human hand or smaller yield the promise of a

wider array of capabilities previously unattainable by their larger UAV counterparts.

Advancements in MAV capabilities will greatly enhance mission possibilities for both terrestrial

surveillance and reconnaissance missions and remote sensing applications for planetary space

exploration. The classification of MAVs, their role in the global air transportation system,

human factors concerns relating to MAV design and operation, and low Reynolds number

aerodynamic flight design are developing areas of MAV aircraft operations that are vital to their

success for terrestrial military and civilian applications. The characteristics of MAVs which

cause them to be ideally suited as planetary space exploration vehicles are embodied by the

Georgia Tech Research Institute’s Entomopter MAV prototype. Exploration of extreme

environments both on Earth and Mars has the potential to be significantly enhanced through the

continued expansion of MAV aircraft and the small scale technologies associated with them.

DARPA Micro Air Vehicle Guidelines

The United States Micro Air Vehicle program has largely been driven by mission needs

of the Department of Defense. The Department of Defense has identified a need to develop

“autonomous, lightweight, small-scale flying machines that are appropriate for a variety of

missions including reconnaissance over land, in buildings and tunnels, and other confined

spaces. Of particular interest is the ability of these vehicles to operate in the urban environment


and perch on buildings to provide situational awareness to the warfighter.” (Pines & Bohorquez,

2006, pg. 290). An MAV design useful to the DoD’s needs additionally requires a cost effective

and efficient design that is simple to deploy and operate. The design and production of a micro

sized aircraft is a challenging prospect even with recent advances in small scale electronics and

materials. Therefore, the DoD initiated a series of MAV design challenges through the Defense

Advanced Research Projects Agency (DARPA) which provides development funding for the

most promising MAV prototype aircraft.

A Micro Air Vehicle (MAV) is a class of unmanned aerial vehicle (UAV) that has a

wingspan of 15 cm or less according to current DARPA Micro Air Vehicle program guidelines.

The first successful MAV was awarded a DARPA Small Business Innovation Research Phase 1

contract in 1996 and a Phase 2 contract in 1998 when AeroVironment successfully demonstrated

its electrically powered flying wing, the Black Widow MAV (Grasmeyer & Kennon, 2001). The

initial design challenge for MAVs was created in order to drive technological development of the

smallest aircraft possible that could operate a successful remote sensing mission. DARPA has

also initiated a program to develop even smaller Nano Air Vehicles (NAVs) which have similar

flight characteristics to MAVs, but NAVs have wingspans of 7.5 cm or less. However,

DARPA’s Nano Air Vehicle program has achieved fewer mission capable prototypes than its

MAV programs as power sources and materials needed for nano-sized aircraft are still not

efficient enough to produce aircraft able to perform a remote sensing mission. The

AeroVironment company was eventually awarded the DARPA Nano Air Vehicle SBIR Phase 1

contract in 2008 and SBIR Phase 2 in 2009 with its demonstration of a controlled, hovering, dual

flapping-wing NAV, the Hummingbird Nano Air Vehicle, which is still in the testing phase


(AeroVironment, 2009). The initial DARPA requirements for an MAV are as follows in Table

1.

Specification Requirements DetailsSize <15.24 cm Maximum dimensionWeight ~100 g Objective GTOWRange 1 to 10 km Operational RangeEndurance 60 min Loiter time on stationAltitude <150 m Operational CeilingSpeed 15 m/s Maximum flight speedPayload 20 g Mission dependentCost $1500 Maximum Cost

Table 1. MAV Design Requirements (Pines & Bohorquez, 2006, p. 292).

The FAA regulates the national airspace and the air transportation system. The FAA has

integrated existing UAVs into the national airspace under the classification of unmanned aircraft

systems, which is defined to include unmanned aerial vehicles and micro air vehicles (FAA,

2013). Analysis of the current air transportation system and the human factors associated with

UAVs is directly applicable to MAVs due to their joint classification as unmanned aircraft

systems.

The Air Transportation System: FAA National Airspace Regulations for Unmanned Aerial

Vehicles

The need for FAA certification and oversight of UAVs has grown since civilian and

commercial interests have increased along with overall UAV capabilities. The Teal Group

estimates that governments and businesses will spend $89 billion on UAV systems through 2023

(Werner, 2014). The FAA has created a comprehensive plan describing the requirements for

integrating UAVs in the present national airspace, and the FAA Modernization and Reform Act

of 2012 sets benchmark dates for integration. The FAA Modernization law fails to set a deadline

for regular UAV flight operations. The law is further limited by the assumption that UAVs will

be operated by human pilots flying the aircraft from external ground sites without any mention of


use of autonomous or semi-autonomous control interfaces. Thirteen U.S. states have already

passed laws restricting UAV operations because of safety or privacy concerns with more states in

the process of following suit. The Aerospace Industries Association expects that conducting

routine UAV operations with an FAA filed flight plan without additional restriction will become

a reality sometime beyond 2025 (Werner, 2014).

The United States Congress included “unmanned aerial vehicles” in the wording of their

2003 Vision 100 – The Century of Aviation Reauthorization Act which outlines specific areas of

FAA development necessary for the Next Generational Air Transportation System (NextGen) to

accommodate the certification and operation of technological improvements to the present

national airspace. The FAA’s current policy on UAV flights was issued in 2007 and prohibits

operation of any UAV flights in the national airspace without a specific authority. The policy

pertains to both public and private unmanned aircraft. The FAA has employed two methods of

granting authority to operate UAVs: Certificate of Waiver of Authorization for private entities

and special airworthiness certificates for public entities to test their experimental stage aircraft.

The concern for these case-by-case basis methods of FAA certification is that the timeline

involved in securing FAA flight permission is much too long to allow the development of

civilian UAV operations (Elias, 2012).

Air Transportation System: Infrastructure Concerns

The Next Generation Air Transportation System Unmanned Aircraft Systems Research,

Development, and Demonstration Roadmap, Version 1.0, identifies the critical areas of the

national airspace that will require updating in order to allow for functional integration of UAVs

(Next Generation Air Transportation System, Joint Planning and Development Office, 2012).

Current communications infrastructure, airspace operations, unmanned aircraft awareness and


certification, and human systems integration are the principal areas of focus for NextGen UAV

development.

At present, there are no communications traffic forecast models for additional UAV

usage. The communications capacity and performance impact of the integration of UAVs into

the existing communications network will need to be defined. Furthermore, civil UAVs will

require a protected safety frequency spectrum for their control system radio signals so as to avoid

signal hacking or interception. Performance requirements and standards for UAV control

systems communication have not yet been outlined by the FAA. Such standardization of

hardware and radio signals would assist the integration of UAVs into the national airspace (Next

Generation Air Transportation System, Joint Planning and Development Office, 2012).

Airspace operations of all current UAVs in the national airspace have maintained

separate automation systems including: collision avoidance, self-separation, and separation

assurance systems. Pilot, air traffic control, and automation roles will need to be better defined

and mandated by the FAA to facilitate the seamless operation of multiple automation systems in

the same air space. Data collection and development of a UAV safety program current does not

exist. Standardized safety analyses of UAVs would allow for useful accident reporting and

would provide a program for addressing UAV safety concerns in the national airspace. Sense

and avoid sensors required for all UAV aircraft are the present solution to avoiding the projected

increase of UAV collisions due to their small size and pilotless functioning. Standardization and

regulation of the sensitivity of such devices is required for their effective use to manage UAV

proximities during airspace operations (Next Generation Air Transportation System, Joint

Planning and Development Office, 2012).


Unmanned aircraft as a modern aircraft platform have design and operational

considerations that differ from those of manned aircraft. Awareness of these UAV differences is

crucial for their integration into the present airspace. UAVs often perform changes to their flight

trajectories multiple times during the course of operations without the benefit of a pilot on board

to oversee them. There exists no national system for tracking such minute changes through

current air traffic control methods. The certification process for UAVs is a time consuming and

outmoded process that does not take into account new and novel materials that have been

development in recent years. The rapid evolution of UAVs and UAV technology requires

updating of the present FAA policies regarding their certification. The unmanned nature of

UAVs relies heavily on the Global Positioning System (GPS) for their guidance, navigation, and

control information. The lack of a pilot on board creates the potential for a GPS error to produce

much greater consequences as there are currently no backup navigation systems accurate enough

for UAV flight control. Research on the potential for accidents produced by GPS errors need to

be performed in order to ensure the safety of the national airspace. The advanced avionics and

control software packages used by UAVs have not been standardized or defined. The safety and

reliability of these systems are important factors which need to be addressed for the complete

integration of UAVs with their manned counterparts in the national airspace (Next Generation

Air Transportation System, Joint Planning and Development Office, 2012).

Lastly, human systems integration is a vital aspect of the present airspace that has not yet

been adapted for UAV operations. Air traffic and airspace information require integration into

UAV ground control stations on a national scale. Particularly, monitoring of aircraft trajectories,

terrain avoidance, and weather will assist in UAV operations in the national air space. The levels

of automation vs. human control during routine UAV flight is not current regulated by the FAA.


Standardization and procedures outlined for automatic piloting of UAVs would help to mitigate

errors associated with use of multiple modes of UAV flight control. Communications and

potential hand-offs of unmanned aircraft between UAV ground control stations require

addressing. Ground Control Stations are not currently regulated by the FAA, and they should

demonstrate their ability to safely operate a UAV in the national airspace. Contingencies for

emergency situations which may arise during UAV operations have not been devised. Reliance

on present manned aircraft procedures and response checklists will not necessarily pertain to the

operations of UAVs especially where datalink or control communications are lost between air

traffic control, the UAV ground station, and the UAV. Better defined human systems interaction

will be a vital aspect for the inclusion of UAVs into the future air transportation infrastructure

(Next Generation Air Transportation System, Joint Planning and Development Office, 2012).

Air Transportation System: Privacy Concerns

The expanded surveillance capabilities of MAVs also increase the privacy concerns that

have been the topic of debate due to more widespread civilian use of UAVs. The sensor

payloads of conventional UAVs can include an array of imaging sensors including cameras and

electro-optical imagers, infrared sensors, synthetic aperture radar, and specialized environmental

sensors (Elias, 2012). This wide variety of sensors allows UAVs to be useful for diverse

applications, but raises concerns over intrusiveness of UAV use particularly during widespread

civilian operations. Privacy groups such as the American Civil Liberties Union and the

Electronic Privacy Information Center argue that use of UAVs with imaging sensors “could lead

to abuses in monitoring, tracking, and surveilling people throughout the courses of their daily

lives” (Elias, 2012, p. 19).


Privacy concerns have largely been raised concerning law enforcement and government

use of UAVs, but there is a growing case to be made for the possibility of commercial use of

drones with the expansion of FAA airspace regulations for UAVs (Elias, 2012). UAVs could

become intrusive in the hands of marketing firms, journalists, and private investigation firms.

Use of UAV surveillance in the commercial sectors is not subject to U.S. Fourth Amendment

rights when data collected is not being used by government organizations; however, it could still

be very intrusive to the civilian public. While the FAA has authority over airspace and flight

operations restrictions, it has an extremely limited authority over specific uses for civilian UAV

use (Elias, 2012).

Human Factors Concerning Operations of Unmanned Aerial Vehicles

Safety data for operational micro aerial vehicles is currently limited to prototype research

and small scope military deployment. However, the concerns faced by the emerging MAV class

are driven by the extensive safety data of operational UAVs to this point. While widespread

civilian use of UAVs remains restricted, the military has been using unmanned targeting drones

and UAV aircraft extensively since the 1950s (Elias, 2012). The Air Force Scientific Advisory

Board identifies the human/system interface as the greatest deficiency in current unmanned

aircraft designs in a review conducted in 1996 (Williams, 2006). More recent multiplatform

UAV studies support this assessment, but specific interface deficiencies have been seen to differ

across the greater array of UAV systems available. Unmanned aerial vehicles have a wide

range of capabilities which give them near limitless potential for use in the military and civilian

sectors alike. Proposed markets for this expanding class of aircraft include scientific data

collection, cross-country transport, and telecommunications services alongside the present UAV

markets of surveillance and defense. The wide array of possible flight services of UAVs are tied


together by the requirements of efficient operation whatever the use (McCarley & Wickens,

2004). In particular, the role of the UAV pilot has come under intense scrutiny with the recent

decision of the FAA to integrate UAVs into the national airspace (Federal Aviation

Administration, 2013). Human error has been found to be a major contributing factor to the

higher accident rates of UAVs as compared to conventional manned aircraft. This is in part due

to the fact that the UAV operator’s task in flying these aircraft is quite different to the piloting of

conventional aircraft in many respects. Current aviation standards and regulations for unmanned

flight in the United States national airspace only allow operation of UAV aircraft on a case-by-

case basis (Elias, 2012). A more thorough understanding of the requirements for the human

factors of all aspects of UAV flight is needed to produce safer and more effective UAVs if they

are to continue to expand into the national airspace.

The Department of Defense conducted a comprehensive ten year review of human factors

in military UAV accidents as mishaps using the Human Factors Analysis and Classification

System (HFACS). The “HFACS is a model of accident causation based on the premise latent

failures at the levels of organizational influences, unsafe supervision, and unsafe preconditions

predisposed to active failures (e.g. UAV operator error)” (Thompson, 2005 p. vi). In the case of

UAV human factors analysis using the HFACS, the focus is made on the aspects of UAV

operations that lend themselves to sources of error which may cause operator error in the first

place. Recommendations for system changes in order to remove the possibility of human error

from the operational system are then made. This methodology has proven more effective than

making changes to crew selection or training in order to compensate for the possibility of

mishaps which are inherent in the functioning of any complex system including UAV operations

(Thompson, 2005).


Unmanned aerial vehicles have demonstrated their capabilities to meet recent military

needs during mission in Iraq as a mixed fleet of no fewer than a hundred UAVS in ten distinct

mission profiles. The accident rates of these aircraft are startling particularly when compared to

manned flight: the United States Air Force’s RQ-1 Predator UAV was found to have a mishap

rate of 32 mishaps per 100,000 flight hours, the United States Navy and Marine Corp’s RQ-2

Pioneer accumulated 334 mishaps per 100,000 hours, and the United States Army’s RQ-5 Hunter

showed 55 mishaps per 100,000 hours. Comparatively, current general aviation mishap rates

average 1 mishap per 100,000 flight hours. The reliability rates of UAVs will have to increase

one to two orders of magnitude before they operate with the equivalent safety of general aviation

aircraft (Thompson, 2005). The RQ-1 Predator, RQ-2 Pioneer, and RQ-5 Hunter UAVs were

examined by one of the most comprehensive reviews of UAV mishaps to date, the Office of the

Secretary of Defense’s UAV Reliability Study which was issued in 2003. Collectively, these

UAV platforms were found to have 17% of their sources of failures be attributed to human

factors. While the total number of UAV mishaps remains much larger than for manned aircraft,

the contribution of human error to manned aircraft is 85% of sources of failures. This has been

attributed to the high degree of automation of systems in UAV aircraft even when remotely

piloted as well as the relatively high unreliability of all of the other systems necessary for UAV

flight (Thompson, 2005).

Operations using modern UAV aircraft are still in their infancy and currently utilize flight

technologies that are often still in the early stages of development. Focusing improvements on

the areas of flight automation and flight systems reliability could significantly reduce overall

human factors related accidents. The Office of the Secretary of Defense’s UAV Reliability

Study suggests that UAV operator situational awareness is often significantly reduced by the


complex nature of the human-machine interface used to remotely pilot UAV aircraft. The report

also recommended enhancements for UAV operator training through simulation in an established

ground control station environment which would help address the limited experience levels of

UAV operators and maintainers (Thompson, 2005).

Manned aircraft have been in operation for long enough to achieve a high degree of

reliability. Human factors concerns still pervade the sources of error in any airframe, but

manned aircraft have been developed through countless design iterations with a strong focus on

management and elimination of human errors and mishaps which might occur during the course

of operations. UAV aircraft present many challenges to human factors design that are quite

different from those found in manned aircraft which occur predominantly because the UAV and

pilot are not collocated (McCarley & Wickens, 2004). Human factors design challenges

primarily include issues with: displays and controls, automated system failures, and crew

composition and training.

Human Factors: Displays and Controls

The separation of a UAV and its operator causes a lack of sensory cues that are available

to the pilot of a manned aircraft. Direct sensory inputs from changes in the flight environment

that the UAV is operating in are instead replaced with artificial sensory information that is

relayed to the UAV operator via datalink containing the UAV’s sensor updates. The form that

UAV sensor information is relayed to the operator varies according to the UAV airframe in

question, but it is usually visual imagery with a severely restricted field of view. Physical

control system information, surrounding visual inputs, and sound are typically unaccounted for

in UAV information sent to its operator. This is referred to as “sensory isolation” experienced

by UAV pilots which is a major obstacle to the human-machine integration required for reliable


UAV flight. (McCarley & Wickens, 2004, p. 1). Solutions to the problem of sensory isolation

have come in changes to the displays and controls of UAVs. Multimodal displays are becoming

more prevalent due to proven improvements on UAV pilot situation awareness. One success has

been in the haptic relaying of turbulence information through the pilot’s joystick. Previously,

disturbances caused by turbulence were only relayed to the pilot through the graphic camera

image shaking on the edges of the field of view. Artificial vibration added to the joystick during

turbulence effects helped to achieve a simulated response that was comparable to that

experienced by manned aircraft pilots. The benefits of quickly relaying turbulence intensity and

timing helped particularly for approach and landing tasks (McCarley & Wickens, 2004).

Multimodal displays help to reduce the overall mental workload throughout the course of

a UAV mission by their ability to convey more flight and payload data of a UAV to its operator

in a shorter amount of time than with traditional displays. Not only does this make them

effective for removing sensory isolation effects from UAV operations, but they can also be used

to relieve UAV pilot fatigue through information overload. Tactile and sound displays are being

used in order to alert operators to system malfunctions and other emergency events. This helps

to remove this important information from the already crowded visual display interface

(McCarley & Wickens, 2004).

UAVs are particularly limited in the bandwidths that can be used to relay sensor

information between the vehicle and pilot. This typically results in low temporal and spatial

resolution images that are transmitted to visual displays. Transmission delays and radio

feedback also serve to reduce the overall quality of the sensor inputs given to the UAV pilot that

impair target tracking and other visually intensive operations. Data bandwidth limits imposed by

small scale UAV designs could potentially be alleviated through use of augmented reality or


synthetic vision systems that are still in the research and development stage. In particular,

augmented reality displays have shown to improve visual acuity through use of artificially

compiled visual inputs which leads to overall improvements in UAV flight control by an

operator (McCarley & Wickens, 2004).

Human Factors: Automated System Failures

The automation of flight controls for UAV aircraft has been a focal point for error

mitigation since the inception of remotely piloted aircraft. While automation is certainly an

important advancement for unmanned systems, it does not always provide greater reliability for

the performance of a UAV. The Global Hawk UAV is one of the largest military UAVs that has

been equipped with a fully automated taxiing, take off, and landing system. However, accidents

still occur involving flight-control automation. Even the most sophisticated automatic flight

control systems are still prone to responses that are difficult to anticipate during flight operations

because all possible contingencies of a given flight are difficult to foresee (Williams, 2006).

There is large variety of the extent to which UAV systems are automated. Many platforms have

very little automation and are flown manually by a pilot using remote stick and rudder controls.

Partially automated UAVs provide flight parameter options through a control interface in the

ground station that are selected by the pilot through the course of the flight. Fully automated

UAVs use autopilot controls using preprogrammed flight coordinates during each phase of the

mission, and the flight progress is monitored by the UAV operator for each phase. Data on the

incident of flight accidents involving human error using autonomous control pinpoint automatic

takeoff and landing procedures during these phases of flight. This is predominantly because of

error associated with transfer of pilot control between manual and autopilot modes. Another

contributing factor is ambiguity about the amount of aircraft autonomy that is integral for the


design of automatic systems that are effective in a wide array of contingences (McCarley &

Wickens, 2004).

Human Factors: Crew Selection and Training

UAVs are controlled through a variety of crew setups depending on the characteristics of

the aircraft and mission parameters. Many military reconnaissance missions require a crew of

two operators: one for payload sensor control and one for aircraft control. The separation of

workloads has been found to decrease the amount of errors and increase efficiency for the

functioning of UAVs regardless of mission where conventional UAV displays are the methods of

control. However, future advancements in displays and controls are projected to allow for

piloting of most UAV aircraft by a single operator or even use of a single operator to effectively

supervise the operations of multiple autonomous UAV aircraft. As display and control

technologies become more advanced, however, there are still other elements of UAV operations

where human factors play a role. Hand-off of controls between crews of UAV operators still

presents an area where errors are routinely made due to challenges with inter-crew

communication and coordination. The selection and training of effective UAV operators has not

been thoroughly developed even in the military. Research studies have suggested a positive

correlation between manned flight experience and remote piloting of the United States Air Force

Predator UAV. Currently, even a private pilot rating is not required for all UAV operators in the

military. Ground school UAV training and simulation have correspondingly not been very well

developed. Adequate training and preparation for UAV pilots is necessary if human error is to

be reduced for any UAV platform operating today or in the future (McCarley & Wickens, 2004).


Human Factors: Recommendations of the Department of Defense

The remote operation of UAV aircraft creates human factors challenges in all aspects of

flight operations. The majority of flight operations research involving the human factors of

UAV flight has been pursued by the Department of Defense to date. Accordingly, the Secretary

of the Department of Defense has mandated reduction of the total number of mishaps and

accident rates by a minimum of 50%. In order to accomplish this, the armed forces have been

recommended to “evaluate and optimize UAV operator selection and training criteria and the

ground control station interface design”, enhance current UAV operator training programs to

“include a specific curriculum emphasis on crew resource management”, perform an analysis of

UAV crew manpower requirements and workstation design, and refocus all Department of

Defense human error analysis “from immediate mechanical failures as the cause of UAV

mishaps to failures in the organizational culture […] for UAVs” (Thompson, 2005, p. vii).

Additionally, technological advancements for the human-machine interface used for UAV

operations show promise. The ability to successfully relay UAV sensor information to its pilot

more effectively will ultimately result in the reduction of pilot sensory overload that pervades the

current UAV interfaces. The improvement of automated UAV systems will also help reduce the

pilot workload. However, a better understanding of the extent to which automated control of

UAV systems is reliable and effective is needed to prevent human error arising from automation

itself. Crew selection and training for UAV crews remains to be well defined or prioritized thus

far. Human factors as a source of UAV mishaps and accidents will only be reduced by the active

pursuit of changes to the operational culture, effective crew resource management, and updates

to displays and controls technology of UAVs.


Statistical Analysis: Micro Air Vehicles as an Emerging Class of Unmanned Aerial Vehicles

Advances in small scale electronics and materials technology have allowed for the design

of increasingly smaller aircraft and aircraft systems. In particular, the field of unmanned aerial

vehicles (UAV) has recently undergone a rapid expansion due to the development of such

technology. Department of Defense research contracts and competitions have driven the

performance characteristics of UAV prototypes to new small scale sizes, and DARPA has

defined a new subclass of UAV called the micro air vehicle (MAV). “As a new class of air

vehicle, these systems face many unique challenges that make their design and development

difficult” (Pines & Bohorquez, 2006, p. 290). The purpose of this statistical analysis is to accept

or reject the hypothesis (H0) that there is a statistically significant difference between the aircraft

flight efficiency and performance characteristics of currently operational MAVs and UAVs. To

give a basis for this comparison, the additional hypotheses for the similarities of flight

characteristics within the individual classes of MAV and UAV must also be determined. The

analysis for variance between performance parameters was conducted using an Analysis of

Variance (ANOVA) for a single factor correlation.

The initial set of data that was analyzed included flight efficiency and performance

characteristics from a representative data set from MAVs which have been successfully

demonstrated during the DARPA Phase I MAV competition. Table 2 is adapted from the full

parameter chart. See Appendix A for the full data set.

Vehicle Properties GTOW, g Cruise Speed, m/sWing/Disc loading, N/m²

Wing span or rotor diameter, cm Endurance, min

Black Widow (AeroVironment) 80 13.4 40.3 15.24 30Hoverfly (AeroVironment) 180 17.5 70 18 13.2LUMAV (Auburn University) 440 5 185 15.24 20Micro-Star (Lockheed-Martin) 110 14.5 70.9 22.86 25Microbot (CalTech) 10.5 5 40 15.24 2.1MICOR (UMD) 103 2 25 15.24 3


Table 2 MAV Design and Performance Parameters. Adapted from (Pines & Bohorquez, 2006, p.

293)

The results of a single factor ANOVA for Table 2 for an α of 0.05, which corresponds to

a 95% confidence level, demonstrates large variances between flight parameters. Overall, the P-

value determined by the ANOVA is 0.0085. This is much less than the selected α of 0.05. This

indicates that the hypothesis that flight parameters within the MAV class are similar is rejected,

or H0≠0. The null hypothesis is rejected for the similarities within MAV characteristics. See

Appendix C for the Single Factor ANOVA Analysis of MAV Design and Performance

Parameters.

Data collected from the American Institute of Aeronautics and Astronautics annual UAV

Roundup was used to analyze UAVs currently deployed in the United States of America by the

flight and performance characteristics of endurance, range, and flight ceiling. As the

characteristics of endurance and range involve the design specifications used in the MAV data

set in their computation, these analysis variables are comparable. Table 3 is adapted from the

full chart of data.


Endurance (hr) Range (mi) Ceiling (ft)Fixed-Wing UAV AAI Shadow 400 5 200 3353

AeroVironment RQ-11B Raven 1.5 6 500AeroVironment RQ-14 Dragon Eye 1 3 152Applied Research Associates Nighthawk 1 6 11000BAI Systems XPV-1 Tern 4 64.4 3048DRS Unamnned Technologies Sentry HP 6 70 10000DRS Unamnned Technologies RQ-15 Neptune 4 50 8000Elbit Systems of America Hermes 450 18 120 18000Elbit Systems of America Skylark I-LE 3 9 15000General Atomics I-GNAT ER/Sky Warrior 40 155 25000General Atomics Predator (RQ-1A/MQ-1) 40 675 25000General Atomics Guardian 27 1151 50000Lockheed Martin Desert Hawk III 1.5 9.3 1000Northrop Grumman Bat 12 12 989 18000Northrop Grumman MQ-5B Hunter 15 166 6096Textron Defense Systems Shadow 200 9 90 15000

Rotary UAV AAI RQ-7B Shadow 200 TUAS 9 125 4572AAI Shadow 600 14 200 4877Guided Systems Technologies SiCX-10E 0.416666667 0.5 12000Kaman Aviation K-MAX UAT 10 115 29000MMIST CQ-10A 15 500 18000

Table 3 Deployed UAV Flight and Performance Characteristics. Adapted from (American

Institute of Aeronautics and Astronautics, 2013 pp. 26-31)

The results of a single factor ANOVA for Table 3, for an α of 0.05, also displays large

variances between flight parameters. Overall, the P-value determined by the ANOVA is 1.23 x

10-8. This is much less than the selected α of 0.05, and more than five orders of magnitude

smaller than for MAVs. This indicates that the hypothesis that flight parameters within the

currently deployed UAV class are similar is rejected, or H0≠0. The null hypothesis is rejected for

the similarities within UAV characteristics. See Appendix D for the Single Factor ANOVA

Analysis for Deployed UAV Flight and Performance Characteristics.

The size range for currently deployed UAVs in the United States is extremely wide

ranging. Therefore, an additional analysis for similarities in the UAV class was completed as a

means of comparing UAVs within the same size range. Small UAVs as determined by the

American Institute of Aeronautics and Astronautics where analyzed for their parameter

similarities. Table 4 Small Size UAV Performance and Flight Characteristics. Additional design


parameter data in the categories of wingspan and maximum takeoff weight were added from

individual manufacturers’ data specification sheets. See Appendix B for the full adapted UAV

chart.

Designation Endurance (hr) Range (mi.) Ceiling (ft) Wingspan (ft)Max Take-Off Weight (lbs)

Orbiter 3.0 31.1 18,000 7.2 15.4

RQ-11B Raven 1.5 6.0 500 4.5 4.2

RQ-14 Dragon Eye 1.0 3.0 152 3.8 5.9

RQ-20A Puma AE 2.0 3.0 500 9.2 13.5

Wasp AE 0.8 3.0 500 3.3 2.9

Bala B 0.8 10.0 10,000 5.4 3.5

Jago B 1.0 20.0 10,000 5.4 12.0

Invenio 0.8 10.0 10,000 4.5 3.5

Nighthawk 1.0 6.0 11,000 2.2 1.9

Skate SUAS 1.5 3.0 400 2.0 2.2

Coyote 1.5 23.0 20,000 5.7 12.1

XPV-1 Tern 4.0 64.4 3,048 11.3 24.3

Dragon 3.0 50.0 10,000 8.0 95.0

RQ-15 Neptune 4.0 50.0 8,000 7.0 135.0

T-Hawk 0.8 3.6 10,000 1.2 20.0

Bat 4 12.0 10.0 10,000 13.0 125.0

Super Bat 10.0 10.0 10,000 8.5 34.0

V Bat 10.0 10.0 15,000 8.0 55.0

SR5 0.3 2.5 1,640 2.3 4.0

SR20 1.3 6.0 4,900 5.1 24.5

Merlin 200 5.5 60.0 11,000 16.0 161.0

Rotor Buzz 1.0 15.0 6,000 11.7 265.0

Silhouette 1.0 7.0 10,000 8.3 28.5

Table 4 Small Size UAV Performance and Flight Characteristics. Adapted from (American

Institute of Aeronautics and Astronautics, 2013 pp. 26-31)

The results of a single factor ANOVA for Table 4, for an α of 0.05, similarly contains

large variances between flight parameters of small sized UAVs. Overall, the P-value determined

by the ANOVA is 6.09 x 10-22. This is much less than the selected α of 0.05. This indicates that

the hypothesis that flight parameters within the Small Sized UAV class are similar is rejected, or

H0≠0. The null hypothesis is rejected for the similarities within UAV characteristics. See


Appendix E for the Single Factor ANOVA Analysis of MAV Design and Performance

Parameters.

The results of all three ANOVA analyzes demonstrate the large variation between flight

and performance characteristics of MAVs and UAVs. While this does not lend itself to

comparing the two classes of aircraft by their characteristics, it does suggest an overall defining

characteristic of unmanned aircraft. Namely, the functionality and capabilities of unmanned

aircraft are extremely wide ranging. DARPA has defined the physical size of MAVs for their

continued small scale research, but both UAVs and MAVs have a vast array of configurations

regardless of their size. It is this characteristic which most defines their potential.

Terrestrial MAV Design Considerations

Micro Aerial Vehicles can operate in a wider variety of environments than their present

UAV counterparts. The U.S. military is particularly interested in MAVs which could have the

ability to explore “underground bunker and other structures” (Georgia Tech Research Institute,

2013, p. 1). The development of an insectoid entomopter MAV design concept is being

developed at Georgia Tech alongside its Mars version and would also provide a hybrid

air/ground platform for surveillance of tight spaces and low oxygen environments on Earth

(Georgia Tech Research Institute, 2013).

Mars Exploration MAV Design Criteria

As the nearest planet to Earth, Mars has been the focus of scientific exploration for the

last twenty-five years. Werner von Braun even considered a rocket design for Mars as early as

1953, but it wasn’t until the success of the Viking program in the 1970s that Mars flight was

considered a real possibility (NASA Institute for Advanced Concepts, 2002). To date, most of

the exploration of Mars has been achieved through use of orbiting spacecraft or surface rovers.


Orbiters can produce imaging data over a wide area and over extended periods of time, but the

data is severely limited in resolution (NASA Institute for Advanced Concepts, 2002). Mars

rovers have more data and sample collection options and can image small areas in great detail,

but they are constrained by their ability to traverse the terrain and obstacles that occur in their

area of operation. An airborne exploration platform would enable high resolution imaging of the

Martian surface over large areas with enough maneuverability to traverse canyons and other

obstacles that have hampered the operations of surface rovers. However, flight on Mars presents

numerous challenges which, until the recent technological breakthroughs of MAV technology,

have made a viable airborne platform impossible.

The Martian environment is particularly inhospitable to traditional aircraft. The Mars

atmospheric density is very low at nearly 1/70th of that found on Earth (NASA Institute for

Advanced Concepts, 2002). This is similar to the Earth’s density at one hundred thousand feet

about sea level (Georgia Tech Research Institute, 2013). Lift is proportional to atmospheric

density, wing area, and forward velocity. Flight in the thin Mars atmosphere in a fixed wing

aircraft would either require a large wing span or a very high velocity in order to generate

enough lift to even leave the surface (NASA Institute for Advanced Concepts, 2002). The Mars

atmosphere is also completely different in composition to that of Earth’s, and it is composed of

ninety-five percent carbon dioxide with only slightly more than one percent oxygen (Georgia

Tech Research Institute, 2013). This makes use of oxygen-breathing engines impractical. The

engines used on Mars are better suited to the atmospheric composition if they are chemically or

electrically propelled. Rotorcraft are also heavily affected by air density and composition. The

speed of sound in carbon dioxide is twenty percent lower than in Earth’s oxygen rich atmosphere

(Georgia Tech Research Institute, 2013). Propellers and rotors, therefore, spin much slower on


Mars while producing lower lift and greater shock waves. Temperatures during the course of the

Martian day vary significantly. They rise to negative twenty degrees Celsius and fall below

negative one hundred and forty degrees Celsius in a single day (Georgia Tech Research Institute,

2013). These extreme temperature shifts make materials and fuel selection a particular challenge

for any aircraft operating on Mars. The lower density, low oxygen environment, and severe

temperatures on Mars are obstacles that have hindered traditional airborne platforms for

exploration of the planet’s surface. MAVs, on the other hand, are particularly well equipped for

flight in these regimes.

Case Study: Georgia Tech Research Institute’s Entomopter for Earth and Mars

Exploration

The flight efficiency of biological flyers far outstrips the performance achieved by even

the most efficient of aircraft manned or unmanned. Recent advancements in small-scale flight

have been achieved largely due to the availability of micro-sized technology that provides the

capability to mimic the flight mechanisms and power outputs of bird- and insect- like flight in

micro air vehicle aircraft. The area of biomimetics has been growing due to recent successes of

small-scale robotic vehicles. The term “biomimetics” refers to any “engineering process or

system that mimics biology” (Paulson, 2004, p. 48). Increases in the flight efficiency of

biomimetic MAVs have already been achieved through the first wave of successful designs for

MAVs which focused primarily on their feasibility for flight with limited operational testing.

Further refinement of MAV technology seeks to take their designs beyond feasibility. The

Entomopter MAV being developed by Georgia Tech Research Institute has been granted

numerous research contracts by the Air Force Research Laboratory, the DARPA/DSO

Mesomachines program, and the NASA Institute for Advanced Concepts to develop its platform


for a variety of future mission operations including use for Earth-based surveillance and as a

Mars exploration flyer (Michelson, 2002).

The term “entomopter” takes its meaning from biological flight modes. Birds fly using a

process called “ornithopter” while the beating of insect wings is the process known as flight by

“entomopter” (Azuma, 2006). Insects are generally much smaller than birds, with body weights

of between one and ten grams. Their wings are on a much smaller dimensional scale which

causes the flight mechanisms of insects to differ from those of birds. Their wing beating

frequency is more than 10Hz higher, resulting in a wing loading of less than 10M/m².

Consequently, the Reynolds number for insect flight is even lower than for bird flight with a

value less than 10³ which produces an extremely low flight speed that is prone to wind and other

atmospheric effects (Azuma, 2006). Georgia Tech Research Institute’s Entomopter design is

based specifically off of the hawkmoth (Manduca sexta) for its wing aerodynamics in order to

benefit from the strengths inherent in insect-like flight (Michelson, 2002). The low wing loading

and low flight speed allow for the Entomopter MAV to be easily carried by the wind, maneuver

with enhanced accuracy, and produce a high amount of lift per power output (Azuma, 2006).

The Entomopter design is particularly well suited for operation in a variety of low

Reynolds number environments. The Department of Defense has an interest in the Entomopter

development program for the future potential for swarms of Entomopter MAVs to rapidly deploy

to areas that have previously been inaccessible to traditional flight platforms such as indoors or

in deeply buried underground facilities. NASA’s interest in the Entomopter concerns its use as a

flyer for future Mars exploration (Michelson, 2008). The environments of both Earth’s

inaccessible locations and the Mars surface have uniquely similar characteristics which make the


Entomopter ideally suited for missions either terrestrial or Martian. See Table 8 for a

comparison of Earth and Mars atmospheric physical properties.


Earth Mars (equator)Gas Composition (volume ratio [%])

N2 78.1O2 20.9Ar 0.9H2O 0~2

CO2 95.3O2 0.1N2 2.7Ar 1.6

Temperature [K] 298 270Pressure [Pa] 1.014 x 105 6.0 x 102

Density [kg/m3] 1.17 1.18 x 10-2

Speed of sound [m/s] 345 220Viscosity coefficient [Pa*s] 1.86 x 10-5 1.36 x 10-5

Acceleration of gravity [m/s2] 9.8 3.78Ratio of specific heats 1.4 1.34Table 8. (Shimoyama, 2006, p.8)

The Entomopter has an anaerobic propulsion system which allows it to fly without the

need for oxygen in its flight environment. Its design includes multiple modes of transport

including flight, crawling, and swimming which give it a versatility for exploration unlike any

other flight platform currently in operation. The design of the Entomopter MAV follows the

hawkmoth’s low Reynolds number flight profile; therefore, it is especially useful for flight in

such regimes which exist on both Earth and Mars. The Entomopter’s size and autonomous

configuration are achieved through use of biomimetic, chemically fueled, reciprocating muscle

tissue which the Air Force Research Laboratory has currently contracted for its fourth generation

of performance refinement and size reduction (Michelson, 2002). The extensive operational

capabilities of the Entomopter MAV multimode autonomous robot for the most extreme of

environments has led to the simultaneous development of two Entomopter prototypes by Georgia

Tech Research Institute, each designed for exploration and operation: the Earth-based terrestrial

Entomopter MAV and the Mars-based Mars Flyer Entomopter MAV.


The Rationale for a Flapping Wing Terrestrial Entomopter

Aerial reconnaissance using conventionally sized unmanned aerial vehicles have been

widely successful for a variety of missions including operations requiring a low possibility of

detection. Even large UAVs are able to avoid detection when flying several thousand feet above

their targets. Their size also allows them to utilize state of the art optics hardware which allows

for real time recording of high resolution video and infrared images on UAV platforms such as

the Predator and Global Hawk. While micro air vehicles have a smaller radar signature for

stealth missions, they are inherently limited by their size when it comes to flight range, weather

hazards, and the payload weight they are able to carry. Therefore, the MAV is a poor choice of

platform for replacement of conventionally sized UAVs for outdoor missions. Their strengths,

however, make them ideal for addressing the need for a reconnaissance platform for indoor

environments of which there are none with the present UAV platform options (Michelson, 2002).

Terrestrial UAV missions have been unable to operate in indoor and constricted space

environments to date. MAVs are small enough in size to operate in these environments, and a

flapping wing MAV would have many advantages over a fixed wing configuration. Fixed wing

aircraft require either large wings, high forward velocities, or powerful engines in order to

produce lift (Michelson & Reece, 1998). Indoor operations prohibit high speed flight as the

MAV must be able to avoid obstacles while maintaining surveillance operations. Larger wings

quickly increase the size of the MAV and ultimately prevent MAV operation in confined spaces.

Turbofans, propellers, and other rotors used to provide air circulation over a wing are inefficient

at power use and severely limit the flight time of MAVs; therefore, they are not ideal for lift

generation of MAVs (Michelson & Reece, 1998).


Flapping wing flight is particularly suited to the operations of confined space MAVs.

Flapping wing flight produced a high lift with a comparatively low power output. Hovering

produced by rotor flight is both inefficient and noisy which is not suited to possible stealth

surveillance operations while hovering produced by flapping is extremely quiet due to a greater

dissipation of the wind vortices produced. Flapping wings are also particularly well suited to

slow and hovering flight which allows for short take-off and landing and greater maneuverability

in confined spaces (Michelson & Reece, 1998).

The Earth-based Terrestrial Entomopter Micro Air Vehicle

The urban indoor mission environment requires multimode vehicles such as the

Entomopter MAV that can fly, crawl, and swim with an autonomous navigation system in order

to rapidly negotiate building infrastructure. The indoor Entomopter MAV surveillance mission

also requires the MAV to operate at a much closer proximity to its target than for outdoor

missions. The increased stealth footprint for a small-scale MAV platform in such an enclosed

location gives it a significant advantage over conventional remote surveillance platforms. The

flight modes of both rotary and flapping wing aircraft are particularly well suited to flight in

confined spaces due to their ability to take off and land vertically and maneuver at a slow

airspeeds. Flapping wing flight has the advantage over rotary wing flight as the mechanical

design for a flapping wing MAV is less physically complex while highly energy efficient when

compared to a rotary MAV with similar capabilities (Michelson, 2002).

The terrestrial Entomopter operates using a highly efficient biomimetic propulsion

system, the reciprocating chemical muscle. “The [reciprocating chemical muscle] is an

anaerobic, ignitionless, catalytic device that can operate from a number of chemical fuel sources”

(Michelson, 2002, p. 484). The reciprocating chemical muscle program at Georgia Tech


Research Institute was originally to determine its feasibility for use in robotics. The program has

undergone additional refinement and size reduction during the DARPA/DSO program to

determine the reciprocating chemical muscle’s ability to meet the flight requirements of the

Entomopter. Its current design iteration demonstrates the capacity to generate enough power and

speed for the Entomopter’s flapping wing flight (Michelson, 2008).

The Entomopter uses flapping wing flight as a means to generate lift. The dual wing

configuration has been modeled after the hawkmoth, but they have been significantly modified

for use in conjunction with the reciprocating chemical muscle propulsive system. The wing

shape has been simplified from the hawkmoth’s mechanically complex structure which allows

for easier manufacture and active flow control during flight. The reciprocating chemical muscle

is connected to the wings, and it produces a wing beat using simple harmonic motion that

produces velocity, yaw, pitching, and roll changes. These flight maneuvers achieved solely

through lift modification through airflow about the wings using the waste gas output of the

reciprocating chemical muscle (Michelson, 2002).

Navigation of the Entomopter using as power efficient a system as possible is crucial to

its successful use for the reconnaissance and surveillance of confined indoor spaces. Complex

navigation systems typically require more power than can be stored on most MAV aircraft to

date. However, the navigation control of an MAV indoors must be as real time and accurate as

possible in order to effectively maneuver around obstacles that the MAV might encounter. Due

to the small size of the Entomopter, long communications antennas cannot be used as they do not

fit on the aircraft. Remote operation of an MAV inside a building is inhibited due to the loss of

transmitters and receivers between walls as well as the inability for a remote pilot to see the

MAV from outside of the target area. Therefore, an autonomous navigation system is the only


viable means of navigation for an indoor reconnaissance mission (Michelson, 2008). The

Entomopter uses a gas operated ultrasonic transmitter for obstacle avoidance and altitude sensing

that works similar to the ultrasonic methods of navigation of bats. Waste gas from the

reciprocating chemical muscle propulsion system creates a frequency-modulated continuous

wave that is picked up by the Entomopter’s finely tuned avoidance system sensors and returned

as a homing signal output. An obstacle in front of the Entomopter will cause an automatic signal

to fly to avoid the nearby area, or it will trigger an automatic all clear signal will be sent in order

to continue along its flight path (Michelson, 2002).

Multiple modes of locomotion make the Entomopter particularly well suited to operations

in varied terrain. The flight capability of the Entomopter allows for navigation through

ventilation systems, doors, or windows. Its crawling capability allows it to maneuver into spaces

where flight is not possible such as under a closed door and around large obstacles. Crawling

also uses less power than the Entomopter’s flight mode, therefore, a mission could be allowed to

continue on the ground once power for flight had been expended. The possibility of damage to

the flight system during operation would also become less of a concern because of the existence

of an alternate method of locomotion. Entomopter designs have even considered the possibility

of including a swimming form of locomotion specifically for navigating through sewers,

however the current configuration only includes the dual mode flight and crawling options

(Michelson, 2002).

Mars-based Mars Flyer Entomopter Unmanned Aerial Vehicle

Exploration of the Mars surface is an important area of space research because it may

provide greater understanding of the physical and biological histories of the planets in the solar

system, which may yield clues to the planetary evolution of planet Earth. To date, two primary


methods of planetary exploration have been employed to remotely observe Mars from Earth:

remote sensing via Mars orbiting satellite and rover data collection from the surface of the

planet. The National Aeronautics and Space Administration (NASA)’s Mars Global Surveyor

satellite was placed into orbit around Mars in 1997 and the European Space Agency’s Mars

Express satellite started orbital monitoring in 2003. These satellites provide image data of the

Mars surface from orbit using wide angle lens cameras which allow for large coverage of the

planet’s surface at the detriment of resolution (Shimoyama, 2006). More detailed imaging of

Mars has been provided through the use of surface rovers. NASA deployed rovers

“Opportunity” and “Spirit” in 2003. They have captured high resolution images which have

provided more detailed mapping of the Mars surface, but their coverage is limited to the small

area that they can traverse. The use of aircraft for Mars exploration is anticipated to be the next

step in Mars surveying due to their ability to provide high resolution remote sensing to a much

larger areas than can be explored by surface rovers (Shimoyama, 2006). While the concept of

using aircraft to observe Mars has many benefits, the reality of designing aircraft that can fly in

Mars’s atmosphere is extremely challenging using present terrestrial aircraft specifications.

The Mars environment is particularly inhospitable to aircraft that can function without

difficulty on Earth. The low density and low oxygen atmosphere of Mars creates a very similar

low Reynolds number flight environment that the terrestrial Entomopter MAV thrives in

(Michelson, 2002). The flapping wings of the Entomopter make it capable of producing high lift

while maintaining the stationary positioning of its fuselage as it moves slowly above the Mars

surface. The flight of such an aircraft would greatly increase imaging capabilities beyond what

is currently capable with surface rovers (Laan et al., 2004). Many of the flight characteristics of

the terrestrial Entomopter MAV will be used for Mars exploration, however, the physical


dimensions are being scaled up to a small-scale unmanned aerial vehicle in order to facilitate

sample collection and a greater maximum range. The proposed size increase enlarges the wing

span of the Mars Flyer Entomopter to 1 meter which would allow it to operate with a Reynolds

number comparable to that of insect flight on Earth (Michelson & Navqi, 2003). The anaerobic

propulsion system selected for the terrestrial Entomopter MAV will also be scaled up for the

Mars version as it can operate in a low oxygen environment using multiple fuel sources

including hydrazine which is currently used on spacecraft (Michelson, 2002). Current wing

design dimensions for the Mars Flyer Entomopter are shown in Table 9.

Wing Span 1 mAspect Ratio 5.874Wing Area 0.546 m2

Table 9. (Michelson & Navqi, 2003, p. 8)

The NASA Institute for Advanced Concepts has outlined several possible mission

structures for the exploration of the Mars surface using the Mars Flyer Entomopter. The

dimensions of the Entomopter are limited by the payload specifications of the launch vehicle

used for both launch to Mars and by the payload specifications of the Mars lander used. The

Arianne 6 launch vehicle has been studied for compatibility with the Entomopter, and the need

for folding wings to reduce width requirements has been theorized. The specifications of the

Mars mission that will deploy and operate the Mars Entomopter are currently unknown;

therefore the design of the Entomopter configuration has been made to be adaptable to any of the

following possibilities: exploration within range of a central vehicle, independent exploration

using an Entomopter, and an Entomopter that works in tandem with a rover (Michelson, 2002).

The mission of the Entomopter operating within range of a central vehicle assumes a

Mars lander containing several Entomopters descends to the Mars surface and deploys the

vehicles. The lander is then used as a base of operations for relaying communications between


the Entomopters and Earth, refueling of Entomopters between surveying tracks, and for storage

of image data and samples collected during the course of the mission. The primary disadvantage

of the central lander scenario is that the exploration area of the Mars surface is limited to the

round trip range of the Entomopters around the fixed area of the central lander (Michelson,

2002).

The second possibility for the design of the Entomopter mission to Mars increases the

range of surface exploration to the full range of the Entomopter through independent exploration.

A lander is still used to descend to the surface of Mars and deploy the Entomopters. However,

this scenario requires that the Entomopters themselves are capable of relaying telemetry back to

Earth. The lander only serves as the initial transportation to the planet. The Entomopters for this

mission are one-way and expendable unless some way of harvesting fuel from the Mars

environment can be found. The advantage of the independent Entomopter exploration mission is

that the effective range of the Entomopters is doubled from the central lander scenario.

However, the ability to return to a previously explored area or the collection of environmental

samples is not an option (Michelson, 2002).

Lastly, the exploration of the Mars surface may be accomplished through a tandem

system in which an Entomopter operates in conjunction with a rover. A lander containing either

a single or multiple Entomopters as well as a surface rover descends to the surface of Mars. The

vehicles are deployed. The central lander operates as a vehicle transport, communications relay,

and operates as a refueling station for the rover. The rover, in turn, serves as the Entomopter’s

base of operations and transfers fuel for the course of the mission. The Entomopters relay

telemetry and communications with the rover which then relays the information to the lander.

Rover navigation is enhanced using Entomopter mapping data throughout the course of the


mission. The feasibility of this mission profile relies on several key factors. The rover itself

must be able to recharge during the mission through solar panels and battery storage. The

chemical fuel for the Entomopters must be able to be transferred from the central lander to the

rover and then stored on the rover. The principal advantage to the tandem operations scenario is

that the Entomopters can explore new terrain on a daily basis while the surface rover slowly

advances across the Mars surface (Michelson, 2002).

Low Reynolds Number Aerodynamics

The flight of birds, bats, and insects occurs in the flight regime known as low Reynolds

number flight. Out of thirteen thousand warm blooded vertebrates, ten thousand of them fly.

Insects are even smaller flyers, and there are nearly one million species of flying insects (Shyy,

2008). Aerodynamics research and aeronautical technology have advanced dramatically over the

last century, but flight performance of aircraft is still far behind nature’s flying machines which

have evolved their flight characteristics over the span of roughly one hundred and fifty million

years. The top speed of humans is about four body-lengths per second, the top speed of horses is

nearly 7 body-lengths per second, and the SR-71 Blackbird stealth aircraft manages 32 body-

lengths per second at Mach 3. It is astonishing that the common pigeon can attain speeds of

seventy-five body-lengths per second, and the European starling flies at one hundred and forty

body-lengths per second. The maneuverability and resiliency of birds in flight is also quite

amazing. The roll rate of one of the most maneuverable aerobatic aircraft, the A-4 Skyhawk, is

720º per second while the Barn Swallow has a roll rate of over 5000º per second. Military

aircraft can withstand up to ten times the gravitational force, while many birds routinely

experience up to 14 gravitational forces during flight (Shyy, 2008).


Micro aerial vehicles operate in a very sensitive flight regime due to their small size and

weight where the flight characteristics of MAVs are subject to low Reynolds number

aerodynamics. At low Reynolds numbers, lift is particularly difficult to produce across a wing as

the more the airflow separates it becomes affected by a multitude of complex flow phenomena

that are not present in the smooth flow that occurs during more traditional flight at higher

Reynolds numbers (Pines & Bohorquez, 2006). Until recently, there has been a lack of

knowledge about the fundamentals of flow aerodynamics at low Reynolds numbers. The only

flying “machines” that have been able to successfully operate at such low airflow have been

birds and insects. Accurate modelling of these natural low Reynolds number flyers has led to the

greater understanding of non-traditional lift generation, and this has allowed for the first

prototypes of small-scale mechanical flying machines to operate successfully (Pines &

Bohorquez, 2006). The necessary study of bird and insect flight aerodynamics has given rise to a

new type of flight design suitable for flight at low Reynolds numbers called biokinetic flight, as

the flight mechanisms mimic naturally occurring biological characteristics (Azuma, 2006).

The high performance, maneuverability, and structural resilience of biological flyers has

been sought after since the beginnings of flight experimentation, but these capabilities have

proven difficult to replicate by manmade flying machines (Shyy, 2008). However, recent

advancements in materials and control technologies along with a greater understanding of

biological aerodynamics have finally brought the possibility of developing aircraft which can

operate in the same flight conditions as bird and insects. Research in these MAVs in low

Reynolds number flight has given rise to greater performance and efficiency with every design

iteration.


MAV flight shares several major unique characteristics to biological flight despite

differences in flight mechanisms. See Appendix F for this comparison of characteristics in the

Great Flight Diagram. The small size of MAVs causes them to operate in low Reynolds

numbers (104-105) which leads to degraded aerodynamic performance. The overall small

dimensions and weight of MAVs result in greatly reduced payload capabilities. However, the

reduced structural dimensions also have the effect of increasing overall structural strength when

loaded, reducing stall speed, and increasing the overall impact tolerances of the aircraft (Shyy,

2008). MAVs also have a much lower flight speed due to their smaller scaling which results in

unsteady flight characteristics due to weather effects and wind gusts as well as perturbations in

the flight environment such as those caused by flow separation effects (Jacob, 2010). Efficient

operation in low Reynolds number environments requires use of nontraditional airfoil shapes

compared to the standard thicknesses, amounts of camber, and aspect ratios used in larger

manned aircraft which operate in much different flight conditions (Shyy, 2008). This is

demonstrated by the relationship between the parameters which make up the Reynolds number

which is fundamental to understanding the physical limitations of MAV flight.

The Reynolds number is a dimensionless expression of the relationship between the

variables involved with flow of a fluid over an airfoil. By definition, the Reynolds number is the

ratio of inertial forces to viscous forces on an airfoil in a fluid (Pines & Bohorquez, 2006). For

traditional, steady-state, aerodynamic conditions this ratio is given by Equation 1 where ρ is the

density of the fluid, V is the velocity of the fluid over the airfoil, µ is the viscosity of the fluid,

and c is the characteristic airfoil chord length.

ℜ= ρVcμ

Equation 1. (Pines & Bohorquez, 2006, p. 295).


Low Reynolds number flyers are subject to aerodynamic scaling laws which relate to the

basic steady-state equation for the Reynolds number, Equation 1. The smaller in size the flyer is,

the faster it must flap its wings in order to generate enough lift to stay airborne (Shyy, 2008). A

reduction in size also causes the flyer to experience lower wing loading from the surrounding

airflow. A slower cruising speed, lower stall speed, and greater crash landing resiliency also

result from a reduction in dimensions. In turn, low Reynolds number flyers become more

susceptible to the flight environment caused by weather conditions and wind gusts, and their

steady stream flight produces a reduced lift-to-drag ratio. The complex flight dynamics of

biological flyers mitigate this lift reduction by flapping their wings in order to generate

additional lift and maneuverability in turbulent flight conditions. An overall reduction in weight

causes a decrease in the amount of “fuel” storage possible for the biological flyer and subsequent

need to refuel frequently (Shyy, 2008). The weight of fuel and power output from small

dimensioned power sources remains a significant design challenge for mechanical low Reynolds

number flyers such as MAVs.

Biological flyers such as birds and insects can generate lift forces anywhere from two to

twelve times their body weight (Pines & Bohorquez, 2006). Steady-state aerodynamics fails to

explain this large amount of lift with respect to size. Current research has been focused on

modelling the flight behavior of the most aerodynamically efficient biological flyers in wind

tunnels in order to understand the more complex aerodynamics associated with their flight. The

pervading theory that is emerging has concluded that the wing pitching/plunging/lagging motion

of birds and insects is responsible for the extreme increase lift that cannot be explained by

traditional steady-state aerodynamic theory. Biological flyers also have the ability to change

their wing shape during flight to accommodate complex flow conditions which can


instantaneously alter the aspect ratio, wing warping, and camber length in mid-flight (Pines &

Bohorquez, 2006). To date, no mechanical aircraft has had the ability to alter its wings so

fundamentally. Aircraft have had to rely on set flight mechanisms in order to achieve sustained

flight that remain the primary limitation during aerodynamic and weather related airflow

changes. MAV aircraft are no exception despite advances made in mimicking biological flight

mechanisms. MAVs have wing configurations which allow for sustained flight in low Reynolds

number environments which include fixed-rigid wing and rotary-wing conventional designs as

well as biologically mimicking flapping and flexible wing designs.

Fixed-Rigid Wing Micro Air Vehicle Performance

Fixed-rigid wing Micro Air Vehicles have designs that resemble their traditional manned

airplane counterparts, but they have been scaled down in order to meet the size requirements for

MAVs. Their aerodynamics can largely be explained by steady-state aerodynamics, but they are

extremely prone to turbulence and weather related effects due to their small size. The

performance characteristics for fixed-rigid wing MAVs are linked to their engine and

aerodynamic efficiencies just as they are in traditional aircraft. The endurance of fixed-rigid

wing MAVs at cruising speed is given by the conventional endurance equation for an aircraft in

steady-state conditions powered by an internal combustion engine where E is the endurance, η is

the propeller efficiency, c f is the specific fuel consumption of the engine, CL is the coefficient of

lift, CDis the coefficient of drag, ρ∞ is the density of the fluid (air at a given altitude for an

aircraft), S is the wing surface area, W f is the final weight of the aircraft (typically the weight of

the aircraft minus fuel consumed), and W 0 is the initial weight of the aircraft.

E= ηc f

CL

32

CD√2 ρ∞ S ( 1

√W f

− 1

√W 0) Equation 2. (Pines & Bohorquez, 2006, p. 292)


Equation 2 determines the design characteristics of fixed-rigid wing MAVs that must be

taken into account in order to achieve the maximum power and efficiency possible for an MAV

during flight. The lift coefficient of the wings, propeller efficiency, and wing loading must be at

a maximum and its wing drag coefficient and operational altitude must be at a minimum to

achieve the greatest possible endurance performance for the MAV (Pines & Bohorquez, 2006).

Rotary-Wing/Vertical Take-Off and Landing Micro Air Vehicle Performance

The performance of rotary-wing/vertical take-off and landing (VTOL) vehicles is well

studied for full-scale rotorcraft under steady-state conditions, but the actual performance of

MAV rotor efficiency in hover at low Reynolds numbers is less understood. Therefore,

performance characteristics for rotorcraft MAVs rely on a comparison of design-determined

ideal power output compared to the actual power output required to maintain hovering flight

given by Equation 3 where FM is the rotor Figure of Merit, T is the thrust, υ is the rotor induced

velocity, and P is the power supplied to the rotor by the engine:

FM=ideal power

actual power=Tυ

PEquation 3. (Pines & Bohorquez, 2006, p. 294)

The ideal rotorcraft would have a rotor that functioned without mechanical or

aerodynamic losses and have an FM of 1. However, a rotorcraft MAV functioning in actual

environmental conditions can only hope to minimize its losses while maximizing its thrust. This

will be seen by an FM as large as possible. Using the basic momentum equation, the relationship

of power loading (P.L. = Power/Thrust) to disk loading (D.L. = Thrust/Disk Area) can be derived

from the FM relationship and yields the power efficiency of hovering aircraft:

P . L .=0.638D . L .FM

Equation 4. (Pines & Bohorquez, 2006, p. 294)


Rotorcraft MAV designs face significant challenges when scaling a conventional rotor

configuration down to MAV size constraints. Drag losses rapidly increase at low Reynolds

numbers which greatly reduce the lift-to-drag ratios for MAV rotorcraft. The airfoil thickness of

the rotor blades increases the disk loading at smaller dimensions, therefore, conventional rotor

airfoil shapes cannot be used. Surface roughness of the rotor also plays a significant role in the

performance of MAV rotorcraft at low Reynolds numbers as it increases the overall drag on the

rotor disk (Pines & Bohorquez, 2006).

Flapping Wing Micro Air Vehicle Performance

Performance characteristics for Micro Air Vehicle wing designs that are based on

biological flight have more complex relationships than for fixed- and rotary-winged MAVs.

Elements of conventional fixed and rotary wing flight that generally contribute to an increase in

drag and subsequent decrease in efficiency during conventional flight can suddenly become

assets to more biologically adapted flapping wing flight. The current power and efficiency

equations for flapping winged MAVs have been approximated using modelling data derived

from bird and insect flight in a wind tunnel. Insect flight is particularly efficient and is

characterized by four mechanical phases: an upstroke, a downstroke, a pronated rotation, and a

supinated rotation (Pines & Bohorquez, 2006). See Appendix G for the Components of Insect

Flapping Wing Flight. Because of insect flight’s high efficiency, it has been more intensely

studied as a means of efficient flapping wing MAV flight than less efficient biological flight

mechanisms. Insect wing performance can be approximated using the mean Reynolds number

for an insect wing given by Equation 5 where Re is the mean Reynolds number, Ф is the

wingbeat amplitude from peak to peak, f is the wing-beat frequency, R is the wing length/span,

and AR is the aspect ratio of the wing:


ℜ=4 Фf R2

υAREquation 5. (Pines & Bohorquez, 2006, p. 296)

Insect flight performance is remarkably efficient despite its low Reynolds number flight

environment. Insects of varying sizes fly in Reynolds number regimes between 10 and 104

(Pines and Bohorquez). For flapping wing flight, the airflow exhibits predominantly

conventional steady state aerodynamics with the benefit of several complex unsteady flight

mechanisms which improve flight performance (Pines & Bohorquez, 2006). The study of these

unsteady flight mechanisms show promise in explaining how flapping wings can generate such

large lifting forces over a complete wing flapping cycle. Under steady-state aerodynamic

conditions, a high angle of attack of a wing against the direction of airflow velocity will create an

increase in pressure near the leading edge of the airfoil, and causes flow separation and

turbulence (Pines & Bohorquez, 2006). This typically increases the overall drag on a

conventional aircraft wing. However, in the case of flapping wing flight, this flow separation is

used to increase lift. When flow separation occurs due to an increased angle of attack, a vortex

forms just in front of the wing and begins to move along the upper surface of the wing along the

path of the airflow. The force of the additional vortex induces a pressure wave which can

provide additional lift and help to produce airloads many times over those found on a

conventional rotorblade or wing in steady state flight (Pines & Bohorquez, 2006). While insect

wing beating can be related to helicopter dynamics in the upstroke and downstroke phases,

additional unsteady flight mechanisms in the remaining two rotational phases are the most likely

to provide the extremely enhanced lift seen in wind tunnel testing. The rotational phases of

insect flight involve pronation and supination of the wings which lead to an increase in lift

provided by the unsteady flight mechanisms: delayed stall due to flow separation, wake capture

which helps to generate additional power from turbulent airflow, rotational circulation which


increases lift, and bound circulation which also serves to generate additional lift (Pines &

Bohorquez, 2006). However, these crucial unsteady flight mechanisms remain little understood

by modern aerodynamics. The challenge of producing a viable flapping wing MAV relies on

further investigation of unconventional aerodynamics. Flapping wing MAVs to date have relied

on mimicking naturally occurring wing shapes and wing beating cycles as closely as possible in

an attempt to reproduce the considerable increases in aerodynamic performance attained by

insects.

Flexible Wing Micro Air Vehicle Performance

Fixed wing micro air vehicles are typically designed with conventional aircraft structures

despite their smaller scale. Ribs and spars make up the structure of the aircraft which is then

covered with a suitable skin with a low overall weight and high tensile strength and stiffness

(Pines & Bohorquez, 2006). The wings of conventional aircraft do not flap like those of

biological flyers, and conventional construction methods appear to fail to remain strong and

supple enough to withstand the comparatively large aerodynamic forces present in low Reynolds

number flapping-wing flight. Therefore, there is a considerable advantage to using flexible

wings for small-scale flight (Pines & Bohorquez, 2006).

Biological flyers depend on flexible wing structures in order to adapt to the flow

environment. Bird wings, for example, have layers of lightweight and strong feathers which

allow them to quickly adapt their wing shape for a particular flight mode. Bats have even more

complex wing structures than birds. Two dozen independently controlled joints make up each

bat wing, and their bones can withstand large amounts of deformation. They can change their

wing shapes drastically to allow them to manipulate their wing camber and create a three

dimensional wing surface to suit even the most extreme aerodynamic stresses. Bats are


extremely maneuverable and have the ability to flight in both positive and negative angles of

attack with turn rates which exceed 200º per second. Both birds and bats have the ability to

drastically change their wing shape during flight. The wing area and wing span can be quickly

decreased by flexing their wings in order to increase forward velocity or reduce drag during the

upstroke of a wing beat (Shyy, 2008). Nature’s flexible membrane wing adaptations are being

studied specifically for use in MAV design as they provide an increase in maneuverability during

flight which serves to significantly enhance MAV flight performance in the low Reynolds

number regime.

Aerodynamic performance characteristics for flexible wings are difficult to reduce to

simple mathematics, and they are more accurately determined through wind tunnel modelling

using computational fluid dynamics. However, basic structural flexibility can be taken into

account using the same aerodynamic relationships used for flapping flight in the specific case of

a flexible-wing flyer using flapping to generate lift. In order to do this the four phase flapping

wing motions are given by two linear motions: upstroke and downstroke and two rotational

motions that are corrected to take wing flexibility into account: pronation and supination (Nakata

& Liu, 2011). The rotational motions are approximated by a sine wave to describe their motion

during flapping-wing flight where φc1' and α s 1

' are rotational coefficients determined by

experimental data and ω is the angular frequency of the sine wave created by the flexibility of

the flapping wing given by:

φ=φc1' cos (ωt ) Equation 6. (Nakata & Liu, 2011, p. 3)

α=αs 1' sin (ωt ) Equation 7. (Nakata & Liu, 2011, p. 3)

Aerodynamic performance characteristics are determined by the modified Reynolds

number for flexible wing flight in a flapping-wing configuration which is given by Equation 5,


Reynolds number for a flapping wing, where Re is the mean Reynolds number, Ф is the

wingbeat amplitude from peak to peak, f is the wingbeat frequency, R is the wing length/span,

and AR is the aspect ratio of the wing:

ℜ=4 Фf R2

υAREquation 5. (Pines & Bohorquez, 2006, p. 296)

Flexible wing flight is advantageous to both natural and mechanical flyers as it allows for

increased maneuverability and resilience when handling the large aerodynamic forces inherent in

low Reynolds number flight conditions. The flexing of a wing membrane can cause an increase

in forward velocity and minimize drag on the wing. Even fixed-wings with flexible components

can benefit from the increased range of motion which results in more steady state, controlled

flight. Environmental effects such as wind gusts become less problematic for flexible wing

configurations which provide a more stable lift-to-drag ratio than seen by non-flexible rigid

wings. A membrane wing is also more responsive to variable aerodynamic loads along the wing

surface and can actively contract in order to delay stall. Membrane wings have also been found

to increase overall lift by producing flutter vibrations even in steady state conditions and for rigid

fixed-wings as well as for flapping wing (Shyy, 2008). A flexible wing structure has the

potential to greatly increase the lift and aerodynamic performance of MAVs regardless of wing

configuration because of the increases in adaptability to flight conditions which are otherwise

problematic to aircraft operating at low Reynolds numbers.

Wind Gusting and Other Meteorological Concerns

Recent advances in Micro Air Vehicle aerodynamics have yielded prototypes which are

beginning to facilitate both military and civilian mission parameters. While they are functionally

able to carry out their proposed mission capabilities, they must be robust enough to operate in

real world environments which have airflow and weather effects which are not concerns in the


steady state laboratory environment (Zarvoy & Costello, 2010). MAVs are aircraft that have

weights of tens of grams, and are envisioned to be used in the near future for “reconnaissance,

targeting, tagging and tracking, and internal space mapping in high risk areas that may be

dangerous to humans” (Zarvoy & Costello, 2010, p. 1). Their small size and maneuverability

also make them potentially ideal platforms for exploring complex environments such as damaged

buildings. The same environments that MAVs are ideal for are also prone to extremely complex

aerodynamic phenomena due to the erratic behavior of airflows near the ground such as

separation of flow, wind eddies, vortices, and wind shear. Even indoor environments are prone

to such aerodynamic behaviors at MAV flight operating conditions because of temperature

differences caused by ventilation systems, openings such as windows and doors, and mechanical

heat sources. These temperature differences can cause significant air flow phenomena with

velocities as high as 5 m/s that can lead to stall or even total loss of control over an MAV

(Zarvoy & Costello, 2010). MAV designs must be able to operate in the actual conditions in

their field of operation despite their susceptibility to meteorological or aerodynamic effects if

they are to become useful platforms for the military or civilian markets.

MAVs are more vulnerable to environmental disturbances in the airflow because of their

smaller size and speed. Smaller forces are needed to alter a MAVs trajectory, and their effects

have a much more pronounced effect (Klipp & Measure, 2011). It is not unusual for biological

flyers to experience wind gusts with amplitudes double to the surrounding steady state airspeed,

and MAVs can expect similar conditions when flying in the same turbulent atmosphere (Jacob,

2010). The weather conditions for MAV missions will also drastically change the flight

environment particularly if precipitation is present as it will change the surface aerodynamic

properties of an MAV’s wings or rotorblades. Additionally, the expanded mission capabilities of


MAVs would benefit operations in a multitude of geographic locations including: urban, desert,

arctic, and low oxygen locations (Mueller, 2006). Therefore, an MAV must be able to perform

in a variety of conditions with the ability to adapt to temperature and weather related effects

which can have a massive impact on aerodynamic performance.

Micro Aerial Vehicle Control Automation

The small size and weight of Micro Aerial Vehicles pose significant challenges in flight

control. Many currently operational UAVs are piloted remotely by a human control within

eyesight of the aircraft or through visual display information relayed by the UAV. Micro Aerial

Vehicles on the other hand, are difficult to see at distances of greater than 50 meters due to their

small size and are similarly limited by the small onboard energy that can be used for in-flight

video transmission to a remote pilot (Michelson, 2008). MAVs must be light enough to fly

despite their smaller dimensions and lower power outputs which makes them particularly

susceptible to weather and other aerodynamic effects. Sensors and other communications

equipment onboard an MAV are also subject to small size scaling; therefore, antenna aperture

size is also severely limited and communications must be conducted at higher frequencies

(Michelson, 2008). The current drawbacks to MAV control could be mitigated by eliminating

the need for direct piloting through use of fully autonomous flight control.

Fully autonomous MAV control would eliminate many of the current difficulties

associated with UAV external pilot control while providing a variety of advantages. A fully

autonomous MAV would have a greater range of flight per power output by removing the need

for line-of-sight data links to an external pilot (Michelson, 2008). Atmospheric perturbations,

aerodynamic effects, and obstacles would be less of a hindrance with the quicker reaction time

associated with an autonomous control system. MAV surveillance operations would benefit


from greater stealth aspects of the lower bandwidth emissions and jamming resistance needed for

a fully autonomous system. Operations requiring indoor or “urban canyon” environments where

communications are currently impossible could be navigated by an autonomous MAV

(Michelson, 2008).

Conclusion

The natural flight evolution of biological flyers has produced millions of birds and insect

species that are capable of efficient flight in low Reynolds number airflows. Small sized

mechanical technology in the fields of electronics and materials have recently enabled the

successful design of aircraft which mimic this highly efficient biological flight with the creation

of Micro Air Vehicles (MAVs). The high lift produced by MAVs combined with lower power

requirements for flight are the factors which have made them the focus of a frenzy of research

for the past decade. However, MAV development has a long way to come before it is fully

incorporated into the present air transportation system. The limiting factors of MAVs do not lie

in their continuously refined technological advancements but in the integration of human needs

and operations into their design. The Entomopter MAV prototype is a promising concept for

Earth based surveillance capabilities and Mars based exploration. Research and design of the

Entomopter has unlocked many technologies for low-oxygen propulsion and low Reynolds

number flight that would not have been realized otherwise. The future of unmanned aircraft lies

with MAVs whether in development of advanced designs and transportation infrastructure or in

the defining of their ever growing list of mission capabilities. MAV research will help form the

foundation for the next leap forward in unmanned flight.


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Appendix A: Design and Performance Parameters of Some Representative MAVs

Vehicle PropertiesBlack Widow (AeroVironment)

Hoverfly (AeroVironment)

LUMAV (Auburn University)

Micro-Star (Lockheed-Martin) Microbot (CalTech) MICOR (UMD)

GTOW, g 80 180 440 110 10.5 103Cruise Speed, m/s 13.4 15-20 5 13.4-15.6 5 2Wing loading, N/m² 40.3 --- --- 70.9 40 ---Disc loading, N/m² --- 70 185 --- --- 25Wing span or rotor diameter, cm 15.24 18 15.24 22.86 15.24 15.24Max L/D 6 N/A N/A 6 N/A 5Endurance, min 30 13.2 20 25 2 min 6 s 3Hover Endurance N/A 7.3 N/A N/A N/A 3

Power SourceLithium-ion Batteries

Lithium-ion Batteries 2-stroke IC engine

Lithium-ion batteries

Sanyo NiCad N-50 cells

Lithium-ion batteries

Energy Density, W-h/kg 140 140 5500 methanol 150 100 150Hover Power, W N/A 24.5 70 N/A N/A 11Hover FM N/A 0.39 0.41 N/A N/A 0.55

(Pines & Bohorquez, 2006, p. 293)


Appendix B: Small Sized Unmanned Aerial Vehicles Flight Performance Parameters

AAI Orbiter 3.0 31.1 18,000 7.2 15.4 ISR

AeroVironment with DARPA RQ-11B Raven 1.5 6.0 500 4.5 4.2 RSTA

RQ-14 Dragon Eye 1.0 3.0 152 3.8 5.9 Over-the-next-hill tactical R/S

RQ-20A Puma AE 2.0 3.0 500 9.2 13.5 RSTA

Wasp AE 0.8 3.0 500 3.3 2.9 Squad-level R&S

Applewhite Aero Bala B 0.8 10.0 10,000 5.4 3.5 Target - Research

Jago B 1.0 20.0 10,000 5.4 12.0 Flight Training - Research

Invenio 0.8 10.0 10,000 4.5 3.5 MM/MR - Humanitarian aid

Applied Research Associates Nighthawk 1.0 6.0 11,000 2.2 1.9 Platoon/Squad/Fire Team R/S

Aurora Flight Sciences Skate SUAS 1.5 3.0 400 2.0 2.2 Urban ISR - PS - CAS

BAE Systems Coyote 1.5 23.0 20,000 5.7 12.1 ISR - BDA - Envir

BAI Systems XPV-1 Tern 4.0 64.4 3,048 11.3 24.3 Force protection - Sensor dispersion

Dragon 3.0 50.0 10,000 8.0 95.0 RSTA

DRS Unmanned Technologies RQ-15 Neptune 4.0 50.0 8,000 7.0 135.0 RSTA

Honeywell T-Hawk 0.8 3.6 10,000 1.2 20.0 SA - Reconn

MLB Bat 4 12.0 10.0 10,000 13.0 125.0 Surveillance. - Map - R&D

Super Bat 10.0 10.0 10,000 8.5 34.0 Surveillance. - Map - R&D

V Bat 10.0 10.0 15,000 8.0 55.0 Surveillance - Map - R&D

RotoMotion SR5 0.3 2.5 1,640 2.3 4.0 MM/MR

SR20 1.3 6.0 4,900 5.1 24.5 MM/MR

UAV Research Lab Merlin 200 5.5 60.0 11,000 16.0 161.0 R/S, RSTA, SAR

Rotor Buzz 1.0 15.0 6,000 11.7 265.0 R/S, RDT&E, crop dusting

Silhouette 1.0 7.0 10,000 8.3 28.5 R/S, RDT&E, G-L

(American Institute for Aeronautics and Astronautics, 2013, pp. 26-31)


Appendix C: Single Factor ANOVA for MAV Design and Performance Parameters

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

GTOW, g 6 923.5 153.9166667 22615.44167Cruise Speed, m/s 6 57.4 9.566666667 40.18666667Wing/Disc loading, N/m² 6 431.2 71.86666667 3402.398667Wing span or rotor diameter, cm 6 101.82 16.97 9.54492Endurance, min 6 93.3 15.55 132.367

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 90753.57112 4 22688.39278 4.329856045 0.008490793 2.75871047Within Groups 130999.6946 25 5239.987784

Total 221753.2657 29Total 28967591.93 35


Appendix D: Single Factor ANOVA for Deployed UAV Flight and Performance Characteristics



Endurance (hr) 21 236.4167 11.25794 137.6055Range (mi) 21 4704.2 224.0095 107474.4Ceiling (ft) 21 277598 13218.95 1.44E+08

ANOVASource of Variation SS df MS F P-value F critBetween Groups 2.4E+09 2 1.2E+09 25.05164 1.23E-08 3.150411Within Groups 2.88E+09 60 47970884

Total 5.28E+09 62


Appendix E: Single Factor ANOVA for Small Size UAV Performance and Flight Characteristics



Endurance (hr) 23 67.79666667 2.947681159 11.11226509Range (mi.) 23 406.6 17.67826087 381.4908696Ceiling (ft) 23 180640 7853.913043 32236007.08Wingspan (ft) 23 153.4595144 6.672152802 14.45720253Max Take-Off Weight (lbs) 23 1048.35 45.58043478 4483.640168

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 1129750535 4 282437633.7 43.80114282 6.0871E-22 2.45421339Within Groups 709299751.2 110 6448179.557

Total 1839050286 114


Appendix F: The Great Flight Diagram



Appendix G: Components of Insect Flapping Wing Flight


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