Wind Efficient Path Planning and Reconfigurationof UAS in Future ATM

Leopoldo Rodriguez, Fotios Balampanis, Jose A. Cobano, Ivan Maza, Anibal OlleroRobotics, Vision and Control Group

University of SevilleSeville, Spain

Email: lrodriguez15, fbalampanis, jcobano, imaza, aollero@us.es

Twelfth USA/Europe Air Traffic Management Research and Development Seminar (ATM2017)

Abstract—Unmanned Aerial Systems (UAS) integration tofuture airspace is one of the greatest challenges in Air TrafficManagement. The use of UAS for covering wide areas impliesthe consideration of airspace restrictions and static and dy-namic obstacle avoidance. This results in complex shapes thatneed to be partitioned adequately to ensure coverage. Anotherimportant element for consideration in the generation of safeand efficient trajectories of UAS is the wind field. Typically,in severe wind scenarios, wind is considered often a hazardouscondition. However, recent studies show that proper identificationof the wind field could be used to increase the energy efficiencyof the mission. This paper presents a novel method of areadecomposition and partition that ensures coverage by generatinga triangular mesh to optimize the coverage in the presence ofurban areas, airspace restrictions or even the presence of anobstacle. The waypoint sequencing considers the wind field inorder to perform on-line adjustments to ensure energy gainsor to minimize energy losses with the identified wind field. Forthis purpose, an innovative method for wind identification isproposed which analyses the statistical behavior of wind vectorestimates in order to identify specific features and characterizegiven models. Given the design philosophy and architecture, thissystem can be integrated into next generation autonomous UASflight management systems as part of the waypoint sequencingand trajectory optimization functions. A test case in the north-Seattle area is presented, which is simulated using a 6DOF modelwith different wind scenarios which resulted into considerableenergy gains either by heeding the wind field during the waypointsequencing and during the mission execution. Results show thatthere is a significant improvement on the energy efficiency withan energy consumption reduced by 10% in the presence of wind.

I. INTRODUCTION

Numerous ongoing research intend to provide the necessaryrequirements and procedures for the safe integration of UASinto non-segregated airspace in the context of the future AirTraffic Management (ATM) system, proposed in the NextGeneration Air Traffic Management System (NextGen) and theSingle Sky European Research (SESAR). Potential researchand commercial UAS applications including goods delivery,search and rescue, among others, require a precise set ofrules to ensure safety and reliability of the involved actors.In the context of applications which require area coveragein a non-segregated airspace, there are many aspects whichneed to be considered. Smart path planning is a key area thatneeds to be studied in order to ensure that any given task

does not compromise the safety of the airspace in which itis being performed. Current and future ATM imply complexand dynamic areas which represent numerous challenges inmission planning. Regarding light and small UAS, the NationalAeronautics and Space Administration (NASA) has proposedthe development of the Unmanned Air Traffic Management(UTM) [1] system for Low-Altitude UAS as a response tosafely manage UAS in airspace that are not regulated by theFederal Aviation Administration (FAA). This effort involves athe participation of very important partners such as Amazon,Google, Lockheed Martin Corporation, Honeywell, etc. Inaddition of this set of rules, one important concern of theairspace that is managed by the civil authority is the need toprovide with a reliable Detect-and-Avoid capability as exposedby Haessig et al. [2], in which technologies such as Au-tonomous Dependent System-Broadcast (ADS-B) is exploredas a means to resolve potential conflicts with cooperativeobstacles. There are vast studies about such as the onespresented by Cordon et al. [3], [4] and Paczan et al [3], [4].that provide an insight, in a systems perspective, of differentaspects of the integration of UAS in both NextGen and SESARcontexts. In [3] the authors present the requirements of theinterfaces that are needed for the different phases of flightof a UAS including the mission/flight preparation, as definedby the SESAR Concept of Operations (CONOPS) [5]. TheBusiness or Mission Development Trajectory (BDT/MDT) isa key element on the future ATM and UTM operations, sincemission planning, specially in areas in which the air traffic isdense, represents a considerable challenge.

One important element that can be considered in the earlystages of mission planning, and has proven to affect the oper-ation of all types of UAS, is the weather data [6], particularlythe wind field. Nowadays, the use of Commercial-On-The-Shelf (COTS) components permits that the UAS navigationsystems, even those classified as small or very light, providethe users with very accurate information on their state vector.However, the use of sensors to determine accurately the windvector during the flight are expensive and does not providea significant impact neither during the mission planning norduring the early stages of flight, in which accurate knowledgeof the wind field may result into a more comprehensive pathplanning. Different research efforts, such as the one presentedby Langelaan et al. in [7] and Wenz et. al. [8] present methods

for wind estimation with low-cost sensors even consideringhazardous wind conditions. The instantaneous online estima-tion of the wind vector at any stage of flight, or during thepre-fight operations may not be sufficient to take advantage ofthe wind field to increase flight efficiency. The knowledge ofhazardous wind conditions, which can be even inferred withweather reports or by observation, may impose restrictions inthe use of UAS, even in segregated airspace. Nevertheless,the use of wind as a means to harvest energy in order toincrease the efficiency has been a subject of research such as in[9]. By characterizing the wind field and identifying punctualphenomena such as wind-shear or thermals, the UAS may gainenergy that permit to fulfill its mission with less fuel or toincrease the duration of flight. In the cases presented in theliterature [7], [9], and also in those that has been studied by theauthors [10], the identification of wind is performed withoutany restriction in airspace. Therefore, it has been noticed thatthe integration of the wind identification with the path planningproblem has to be taken into account for future ATM and UTMUAS operations.

The path planning problem and wide area coverage repre-sents by itself a challenge if the complex shapes of airspace aretaken into account. The plentiful restrictions, the considerationof static and moving obstacles, specially in operations close tourban areas, and the potential appearance of sudden air trafficrestrictions require complex algorithms to quickly respond tothe eventuality and prioritizing the mission accomplishment.As it is mentioned before, the wind field identification mayresult into the imposition of greater restrictions which will addcomplexity to the area decomposition, path planning (waypointsequencing) or the path re-planning in an ongoing mission.However, if the wind field is identified it also may representan advantage to increase the energy efficiency throughout themission. Area decomposition and path planning problems havebeen widely studied before [11] and the use of algorithmssuch as boustrophedon movements typically represent solu-tions which do not ensure 100% coverage or complex areas.The authors have previously proposed an area decompositionmethod [12] [13] which seeks complete coverage waypointplans and considers potential airspace restrictions regardlessits shape, allowing the reconfiguration of the decomposed areaand the waypoint re-sequencing considering different aspectsor weights.

The purpose of this paper is to merge the area decompo-sition and path planning problem with a wind identificationmethod in order to provide a mission planning and reconfig-uration solution that considers the complex shapes of currentand future airspace to ensure the highest possible coverage,also taking into account the efficiency and duration of themission by acknowledging the identification of the wind fieldas a potential solution.

II. UAS-BASED WIND FIELD IDENTIFICATION

The wind field identification problem, as proposed in [10],involves two stages. One that identifies the instantaneous windfield at a given rate and other that performs a statistical

ybxbzb

yI

zI

xI^

^

^^

^^

r

va

w

Fig. 1. UAS located in an inertial frame.

analysis to these estimates in order to identify wind features,which can be as simple as constant wind or as complex asdiscrete and continuous gusts or wind shear.

A. Wind Vector Estimation

The wind vector estimation is performed with a DirectComputation (DC) method. It consists in the use of the GlobalNavigation Satellite System (GNSS) navigation solution to-gether with the air mass relative speed in order to determinethe wind vector estimate without the use of a Bayesian filter.

Let a UAS to be located in a vector r in an inertial frameI with unit vectors defined as (x̂I , ŷI , ẑI). Heeding a bodyframe located in the UAS center of mass with unit vectors(x̂b, ŷb, ẑb), the wind vector w with components (wx, wy, wz)and the air-mass-relative velocity (absolute airspeed) va areshown in Fig. 1.

From this point, one can infer the total velocity, based onthe computation of the so-called speed triangle ṙ in the inertialframe as shown in (1):

ṙ = va + w (1)

Therefore, the wind vector w can be calculated in the inertialframe as in (2):wxwy

wz

I

=

ẋẏż

I

− (CbI )−1uvw

b

(2)

The ground position with coordinates (ẋ, ẏ, ż)I can beobtained with good precision in the navigation solution of theGNSS system, which typically has a Kalman filter functionembedded to increase the precision. The airspeed components(u, v, w) which are typically given in the body frame can bedetermined with the knowledge of angle of attack and sideslip,or with the use of an multi-axis airspeed sensor as proposedby Wenz et al in [8]. COTS autopilots have the capability tocompute the airspeed, AOA and sideslip which is sufficient todetermine the wind components with relative good precision.

The main source of error in (2) cannot be inferred easilysince the sources of error are independent. However, in [7],the computation of the wind speed acceleration, which canbe also observed in [10], permits the determination of theairspeed measurements as the most important source of error.It has been determined that there is no need of a Bayesian

filter to keep the wind vector estimation error bounded withinacceptable limits (between 0.7 m/s and 1 m/s for low flightpath angles and without GNSS augmentation) [7].

B. Wind Field Prediction

In order to characterize the surrounding wind field, wecan consider a wind field as the “sum” of four features:constant wind in a given direction, wind shear, discrete gustsand continuous gusts. The Wind Identification System (WIS)proposed by the authors in [10] performs a statistical anal-ysis of accumulated wind estimates together with off-boardinformation such as weather reports or a wind database.

Other important considerations that need to be taken intoaccount are that wind measurements are typically distributedaltitude-wise following a Weibull distribution. Given a data setof wind vector estimations W = (W1, ...,Wn), the Weibulldistribution can be expressed as:

f(W) =κ

ν

(W

ν

)(κ−1)eWν κ (3)

where κ and ν are respectively the shaping and scalingparameters of the Weibull distribution.

From here, one can infer the most probable wind speed‖w‖r at a particular location as a function of κ and ν:

‖w‖r = ν(

1− 1κ

) 1κ

(4)

The identification of the Weibull parameters to calculatethe wind speed magnitude is not trivial, therefore, a solutionwhich has been implemented as in [10] is to use a GeneticAlgorithm (GA) in order to estimate the Weibull parametersand to determine the most probable wind speed at a givenlocation. An implementation of the GA to find the Weibullparameters can be found in [10].

Once the Weibull parameters are identified in altitudegroups, a statistical analysis of the running mean and standarddeviation of the wind estimates is performed in order to fitthe wind estimates into feature models either by analyzing thewind magnitude change over altitude with an Empirical PowerLaw (EPL) or by performing a short term Gaussian regressionto characterize complex features. The selected models arebased on the U.S. Military Specification MIL-F-8785C [14],[15].

1) Wind Shear Identification: In the presence of a WindShear, wind estimates show a growth in the airspeed overaltitude as shown in Fig 2. It is represented by the followingexpression:

‖w‖shear = W20ln hz0

ln 6.096z0, 1m ≤ h ≤ 300m (5)

Shear is a typically undesired phenomena in terminal areaoperations for manned aircraft. However, in the case of UASand while operating in the UTM, it can be useful if the surfacelayer is identified properly, i.e. the altitude in which the wind

0 2 4 6 8 10 12

Wind speed (m/s)

0

50

100

150

200

250

300

Alt

itude (

h)

Fig. 2. Wind shear model.

-20 0 20 40 60 80 100 120 140 160 180 200

Distance (m)

0

1

2

3

4

5

6

Win

d s

peed (

m/s

)

Gust length

Gust

am

plitu

de

Fig. 3. Discrete gust model.

speed is decreasing faster allowing a energy gain in the formof speed or altitude.

If a group of most probable wind speeds Ω =Wmp1,Wmp2, ...,Wmpn can be fitted with a polynomialapproximation into the empirical power law, then the WISassumes that there is a presence of shear on the system.By analyzing the components of the wind vector estimates,the wind direction can be inferred by averaging the directioncosines between them.

2) Discrete Gust Identification: The discrete gust can beseen as an increase of the wind velocity magnitude within adistance. The considered model is the 1− cos as in [14], [15]:

‖w‖gust =

0 x < 0Wm

2

(1− cos πxdm

)0 ≤ x ≤ dm

Wm x > dm

(6)

And it is represented in Fig. (3)In order to characterize the discrete gust one can refer to the

work presented by Zbrozek [17] in which the distribution ofdiscrete gusts, while knowing the power spectrum of normalacceleration, can be utilized in order to determine the intensityof the discrete gust by a continuous distribution of Root MeanSquared (RMS) turbulence. Hence, the analytic expression forprobability density distribution of a gust velocity is:

f̂(σw) =

√2

π

1

bexp

(−1

2

(σwb

)2)(7)

Therefore, by observing the RMS distribution of the windestimates one can determine the intensity if the gust is con-sidered a stationary process.

3) Continuous Gust Identification: For the continuous gust,the Dryden spectral representation is used. This representation,also approved in [14], [15] treats the linear and angularwind velocities as spatially varying stochastic processes inwhich each component is defined by a power spectral density.Therefore, the power spectral densities Φ for linear velocities(ug, vg, wg) and angular velocities are shown in (8) and in (9).

Φug (Ω) = σ2u

2Luπ

1(1+LuΩ)2

Φvg (Ω) = σ2v

2Lvπ

1+12(LvΩ)2

(1+4(LuΩ)2)2

Φwg (Ω) = σ2w

2Lwπ

1+12(LwΩ)2

(1+4(LwΩ)2)2

(8)

Φpg (ω) =σ2w

2V Lw0.8(

2πLω4b

) 13

Φqg (ω) =±( ωV )

2

1+( 4bωπV )2 Φwg (ω)

Φqg (ω) =∓( ωV )

2

1+( 3bωπV )2 Φvg (ω)

(9)where σi is the root-mean-square vertical or lateral gust

velocity and Li is an integral length scale of the turbulenceeddies in the ith. velocity or angular component. Ω is thespatial frequency and b represents the aircraft wingspan.

Trying to characterize the spectral density definitions inorder to predict a turbulence is a complex problem thatrequires high computational power and the results may not beuseful for online use. However, a standard Gaussian Process(GP) regression can be incorporated in order to perform ashort-term prediction to determine a covariance vector q(X, x)and a linear prediction p̄(X) of a linear combination of thewind estimates in the ith direction. The expression of theprediction is:

p̄(X) = q(x,X)[Q(X,X) + σ2nI

]−1 Ŵz (10)where q(x,X) is the covariance vector between two obser-

vations at location X, Q(X,X) is the covariance matrix and σ2nis the measurement noise covariance. Additional details on theimplementation of the regression can be found in [10].

III. COMPLEX AREA PARTITIONING CONSIDERING AERIALRESTRICTIONS.

The increased interest of UAS usage for commercial pur-poses reflects on several mission scenarios which surpass theuse of specific waypoint airways. These missions are oftenhandled by the means of a grid decomposition of areas inorder to accomplish complete coverage [11], for instancein crop spraying or aerial photography. As we’ve shown inprevious studies [12] [18], complex scenarios and geographicattributes are not treated properly by the use of a simple griddecomposition of an area. More specifically, coastal area taskswith their numerous no fly zones or complex shores, impose adynamical approach which has been developed in the contextof the MarineUAS project.

Fig. 4. A test case area, north of Seattle. The red polygon defines the missionarea whereas several other restrictions apply, as can be seen in the next figures.

In a test case scenario area as seen in Fig.4, severalheterogeneous UAS have the task of covering the area inorder to obtain sea life information by using their sensors.In order to perform a fair and successful partitioning of theconfiguration spaces for each UAS, respecting their relativesensing capabilities, like their Field of View (FoV), as also asthe strict borders of the area or future airspace restrictions, aninitial Constrained Delaunay Triangulation (CDT) is proposed.

A CDT introduces forced edge constrains as part of the inputand in such a way, complex areas can be triangulated, creatinga triangular mesh. Then, each centroid of every triangle can beconsidered as a waypoint in the flight plan. Moreover, everytriangle can be given a cost, based on several task,area or agentrelated criteria. As we will describe, this cost can be used forwind information and will further facilitate the extraction ofwaypoint flight plans.

Consider the region presented in Fig.4 as C, includingobstacles, as shown in Fig.5. For waypoint list planning, thisarea is treated as a two dimensional grid such that C = R2,where Cobs are the restricted areas and Cfree = C \Cobs isthe area to be partitioned for the UASs. By triangulating andpartitioning Cfree for M vehicles in a sum of triangles (ψ)such that

Cfree =M∑i=1

N∑j=1

ψij , (11)

the waypoint planning is actually a graph search problemof N nodes organized in the CDT. The algorithmic strategiespresented in [13] are not computationally expensive and permitthe online reconfiguration and planning for either detect andavoidance purposes, task updates or emergency situations.

IV. WIND EFFICIENT WAYPOINT SEQUENCING

In order to perform the wind predictions, as describedin Section II-B, wind estimations have to be stored until asufficient number of them allows such process. This processtakes in average 60 s from the moment the UAS goes airborne[10].

During pre-flight phase, the meteorological reports and theuse of external sensors, such as an anemometer, allow anestimation of the predominant wind direction and to do a

Fig. 5. Several low altitude restrictions apply in the area, like those that canbe seen on the bottom right part. Moreover, the system must be capable torespond in any online restriction. Screenshot is a courtesy of SkyVector.com

Start

Preflight Wind

Yes

DetermineStarting Position

Perform Pre-Flight

WEWS

No

PerformInitial

Sequencing

No

Yes

Obtain Wind

Estimates

UASAirborne

Wind Est.Sufficient

No

Yes

Perform FeatIdentification

DetermineWind Direction

DetermineNeighborghs

Weights

AdjustNew Sequence

DetermineTrajectory

Curve

TrackTrajectory

Yes

Fig. 6. High level view of the WEWS algorithm.

initial wind-based sequencing in order to improve the flightefficiency.

The Wind Efficient Waypoint Sequencing (WEWS) processweights the cells of the decomposed area in such a way thatprioritizes the information on the wind field in two stages: thefirst one aims to determine the predominant wind directionin the presence of constant wind, shear and/or discrete gustsand determines a sequence in which the sequence tries tokeep a positive component aligned to the wind velocity. Theother proposed process intents to provide information to theautopilot in order to adjust the trajectory on a waypoint-to-waypoint basis to maximize the use of the wind as a meansof gaining energy. The WEWS high level process is depictedin Fig. 6.

The initial sequencing is considered as an iteration into theShared Business or Mission Trajectory (SBT/SMT) process.However, during the execution of the mission, the trajectorymay suffer adjustments which are to be used in the update

of the Reference Business or Mission Trajectory (RBT/RMT).During anytime during the mission execution, the path mightsuffer from temporary restrictions or even the appearance of anobstacle. The area decomposition and partitioning algorithmsare able to reconfigure the waypoint sequence accordinglyensuring coverage of the area still with the wind informationas part of the weight.

The energy as a function of the trajectory q(t):

Ē(q(t)) =∫ T

0

[c1‖v(t)‖3 +

c2‖v(t)‖

(1 +‖a(t)‖2 −m

g2)

]dt

+1

2m(‖v(T )‖2 − ‖v(0)‖2)

(12)where

m =(aT (t)v(t))2

‖v(T )‖2(13)

v(t) corresponds to the first derivative of the trajectoryfunction:

v(t) ∆= ˙(q)(t) (14)

a(t) is the second derivative of the trajectory function:

a(t) ∆= ˙(q)(t) (15)

and g is the gravitational constant.In general, avionics power consumption is significantly

smaller in proportion to the propulsion energy. Therefore onlykinematic related components are included into (12).

V. TEST CASES, SIMULATIONS AND RESULTS

The area of Fig.4 has been chosen as a test case, by takinginto consideration a simple aerial restriction as can be seenin Fig.5. The area has been partitioned for two UASs, havingthe same FoV but different coverage capabilities. A coveragewaypoint flight plan has been extracted for one of them, havingas a sequence criterion the outer to inner complete coverage(Fig.7). The simulated experiments show that the resultingtrajectories manage to respect the aforementioned restrictionswhile successfully performing the coverage task (Fig.8).

For simulation purposes the UAS with 20% relative areacoverage capability was selected. The simulated platform cor-responds to the Aerosonde UAV (see Fig. 9. Its characteristicsare enumerated in Table I.

TABLE IAEROSONDE UAV CHARACTERISTICS

Length 1.7 mWidth 2.9 mHeight 1.97 mWeight (MTOW) 25 kgMaximum Speed 140 knotMaximum Range 3000 kmMaximum Ceiling 4500 m (14760 ft)

Fig. 7. North Seattle area with a flight restriction on the bottom right.Left: initial partition for 2 UASs, having an 80% and 20% relative areacoverage capabilities. Black triangles represent the initial positions of theUASs, while the red dots represent the waypoints. The two configurationspaces are distinguished by the different shades of grey. Right: Borders tocenter cost applied to each cell and a coverage flight plan has been extractedfor one of the UASs.

Fig. 8. An APM screenshot during flight.

A 6DOF model was selected in order to perform Software-In-The-Loop (SITL) experiments with the WEWS algo-rithms in place. Simulations were performed using theMATLAB/SIMULINK R©. environment together with the Pix-hawk SITL environment.

Three scenarios are considered, the first one decomposesthe area as in [12], sequencing the waypoints from the edgesto the center in order to ensure full coverage. The secondscenario considers the presence of sustained wind. Finally the

Fig. 9. Aerosonde UAV.

X position (m) #104-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

Y Po

sitio

n (m

)

#104

-5

-4

-3

-2

-1

0

1

2

Starting Point

DIrection of sequence

Fig. 10. Initial area decomposition showing the starting point and the winddirection.

TABLE IIFIRST SEQUENCING SIMULATION RESULTS

Flight Duration 3.2 hMedium Altitude 100 m ASLTotal Energy Consumption ≈22385.66 W/hAverage Wind Speed 0 m/sAverage Airspeed 20.93 m/s

third scenario considers shear and gust phenomena.

A. First scenario: zero wind consideration

Taking the UAS located at the bottom left of Fig. 7.The proposed area is decomposed and sequenced initially asindicated in Fig. 10, in which the coordinates are expressedas relative position to the starting point. Note that this basesequencing does not consider prior information of the windfield.

While executing this trajectory the approximated resultedenergy as per 12 in a Software-In-The-Loop (SITL). Theresults of the simulation are shown in Table II

For the energy calculation a standard gasoline engine with adisplacement of 55 CC and a power of 5.6 HP was consideredas a test case. Hence, the avionics consumption is not includedin the calculation.

B. Second scenario: constant wind consideration

For the second scenario, an average sustained wind of 5 m/sis considered with a wind direction of (−90o). This allowsto appreciate the impact of the wind in the initial waypointsequencing. If this wind is considered then the system triesto minimize the amount of times that the trajectory of theaircraft has a negative component relative to the direction ofthe wind. In addition, the sustained wind was identified andcharacterized as per the Weibull distribution indicated in (3).

The resulting sequencing with the online adjustment is de-picted in Fig. 11. In addition, the identified wind velocity withthe corresponding Weibull characterization can be observed inFig. 12.

The simulation results and energy consumption are shownin Table III. It can be observed and improvement of approxi-mately 11% in the energy consumption. Note that during thesimulation the flight mode was on speed control, therefore,the UAS travels a similar distance with a different sequence.Hence, the flight duration is not significantly impacted. Notethat the average airspeed is decreased due to the wind effect.

X position (m) #104-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

Y Po

sitio

n (m

)

#104

-5

-4

-3

-2

-1

0

1

2

Starting Point

Fig. 11. Area decomposition considering sustained east wind.

Time (s)0 2000 4000 6000 8000 10000 12000

Win

d Ve

locit

y (m

/s)

2

2.5

3

3.5

4

4.5

5

5.5

6

Fig. 12. Estimated wind speed over time with a mean value of 5 m/s.

Wind Speed (m/s)2.5 3 3.5 4 4.5 5 5.5 6 6.5

Freq

uenc

y

0

50

100

150

200

250

300

350

400

Fig. 13. Distribution of wind estimates following a Weibull shape.

TABLE IIISECOND SEQUENCING SIMULATION RESULTS

Flight Duration 3.16 hMedium Altitude 100 m ASLTotal Energy Consumption ≈19913.408 W/hAverage Wind Speed 5 m/sAverage Airspeed 16.74 m/s

During the simulation, online adjustments are done in thetrajectory allowing reshaping the remaining waypoint sequenc-ing. The main inconvenience of this is that there are sharp turnsthat may result into higher energy consumption considering thefull dynamics and the actual trajectory tracking.

C. Second scenario: gust wind consideration

The last scenario considers the presence of gust and shear.however, since there are no changes in altitude the shear doesnot affect directly the energy gain. The simulation starts withno knowledge of the wind field and after a minute of flight,the first wind predictions start to occur. Once the systemdetermines the wind velocity and direction (5 m/s, −90o)the algorithm starts to reconfigure the path during the firstpart of the flight. At 3000 s, a discrete gust of 2 m/s occurs

X position (m) #104-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

Y Po

sitio

n (m

)

#104

-5

-4

-3

-2

-1

0

1

2

Starting Point

Fig. 14. Area decomposition considering discrete gust.

Time (s)0 2000 4000 6000 8000 10000 12000

Win

d ve

locit

y (m

/s)

3

3.5

4

4.5

5

5.5

6

6.5

7

7.5

8

Fig. 15. Estimated wind speed over time with a mean value of 5 m/s.

Wind velocity (m/s)3 4 5 6 7 8 9 10

Freq

uenc

y

0

50

100

150

200

250

300

350

400

450

Fig. 16. Distribution of wind estimates following a Weibull distribution.

TABLE IVSECOND SEQUENCING SIMULATION RESULTS

Flight Duration 3.19 hMedium Altitude 100 m ASLTotal Energy Consumption ≈20421.783 W/hAverage Wind Speed 6.24 m/sAverage Airspeed 15.23 m/s

which results into the increase of the wind velocity magnitude.The direction of the wind is also affected hence the systemperforms a more severe reconfiguration of the flight path. Theidentification of the gust can be determined by detecting theincrease of the wind speed (see Fig. 15) and also with thegeneration of two Weibull distributions as seen in Fig. 16.

The simulation results can be observed in Table IV. In herea energy improvement of 8% was observed. Note that thewaypoint sequence starts as in Fig. 10, however as the wind isidentified and the gust is detected there are significant changeson the sequence.

In the two cases, the energy improvement occurred onlyby rearranging the original waypoint sequence. Even thoughsome overlapping of way points may be observed, there is stilla significant improvement by considering the wind force.

VI. CONCLUSION AND FUTURE WORK

The presented algorithms for area decomposition, waypointsequencing and wind identification are a promising combi-nation for generating wind efficient trajectories for UAS infuture airspace. Normally, a decomposition method takes littleconsideration of the wind phenomena as an aid to improvethe energy efficiency. The cell weighting method allows thegeneration online reconfiguration of the intended waypointsequence considering potential airspace restrictions in thecontext of current and next generation airspace. The designedarchitecture allows an easy incorporation into next generationBusiness or Mission Trajectory (B/MT) procedures. The areadecomposition ensures the maximum coverage of a givenarea due to the triangular cells that permit the inclusionof complex shapes, which are given by the surface of thesurveyed area and/or airspace restrictions. The results showsimprovements up to 11% of efficiency with low winds andup to 9% in the presence of gust and shear with onlinesequence reconfiguration. Efficiency is one of the key aspectsin future airspace with more stringent carbon-dioxide emissionregulations, and the wind energy harvesting is a promisingarea for UAS operating in low altitudes in both the UTMand ATM systems. The incorporation of wind identificationand smart area decomposition into UAS flight managementfunctions shall permit a more efficient use of airspace evenin hard meteorological conditions. Future work includes theincorporation of the described system to a trajectory generationsystem in order to determine, on a waypoint-to-waypoint basis,the 4D optimal trajectories for maximum energy gain given awind field. This will allow a more realistic quantification of theenergy harvested to the wind by generating soaring trajectorysolutions even for the sharp turns that the sequencing systemgenerates. The safety and reliability of UAS systems includingCOTS components allow identification of the wind vector andwind field with the necessary precision to perform the initialsequencing, online reconfiguration and trajectory optimization.In addition, a full test campaign is being prepared in orderto validate and verify the different functions for safe-longduration missions.

ACKNOWLEDGMENT

This work has been supported by the MarineUAS project(MSCA-ITN-2014-642153), funded by the European Com-mission under the H2020 Programme as part of the MarieSklodowska Curie Actions and the AEROMAIN project(DPI2014-5983-C2-1-R), funded by the Science and Innova-tion Ministry of the Spanish Government.

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