Welcome

THE UNIVERSITY OF TEXAS AT ARLINGTONForum: ‘Controls and Uses of Autonomous Vehicles'

Welcome Remarks by President Karbhari

Overview by Jim Grover

Presentations by‒ Frank Lewis‒ Yan Wan‒ Animesh Chakravarthy‒ Nick Gans

Share your research initiatives

Group Activity

Today’s Agenda

Forum on Controls and Uses of Autonomous Vehicles

James P. Grover, Ph.D.Interim Vice President for Research

Autonomous Vehicle Types

http://www.ciobulletin.com/retail/amazon-prime-air-drones

Unmanned Aerial Vehicle (UAS) Unmanned Ground Vehicle (UGS)

http://theconversation.com/what-if-autonomous-vehicles-actually-make-us-more-dependent-on-cars-98498

Unmanned Surface and Underwater Vehicle (USV and UUV)

https://www.unmannedsystemstechnology.com/wp-content/uploads/2013/12/C-Enduro-USV1.jpg

https://www.dezeen.com/2018/03/27/mit-reveal-life-like-soft-robotic-fish-documenting-marine-life-technology/

Broad Applications

Precision Agriculture

Aerial Taxi Cargo Transport

Sports Coverage

Emergency and Disaster Response

Personal Assistance

Traffic Surveillance Land Survey

Infrastructure Health Monitoring

Environmental Monitoring

Business Trend

UAV market is expected to grow US$ 51.85 billion by 2025 from US$ 11.45 billion in 2016.

Global Autonomous Vehicles market accounted for $27.09 billion in 2017 and is expected to reach $615.02 billion by 2026 growing at a CAGR of 41.5%.

Multi-disciplinary Techniques• Autonomous vehicles are integrated systems that are service

oriented, and hence require multi-disciplinary techniques that span– Sensors– Communication– Control – Mechanical and electrical systems– Human-machine interaction– Security, privacy, certification– Data science– Machine learning

– Embedded systems– Cyber-physical systems– Intelligent Transportation– Various application domains

in civil engineering, business, biology, environmental science, urban planning, etc.

UTA Unique Position• We have broad engineering disciplines that offer all the

relevant expertise, including the only Aerospace program offered in the DFW area.

• Located in DFW, surrounded by leading industry players in the autonomous vehicle domain, including Bell, L-3, Lockheed, AT&T, Toyota, Airbus, Boeing, etc.

• Solid track record with sustained success in funding and funded research

UTA Track of Record• Extensively funded research in

this domain from NSF, Lockheed, Ford, ONR, ARO, AFOSR, AFRL, FAA, etc.

• Winning national competitions such as AFRL Search and AI challenge

• Close established collaboration with local agencies and industries for successful technology transfer

Federal Recognition• Pentagon Unmanned Systems Integrated Roadmap highlights the needs of DoD

in the near and future term: advanced autonomy, manned/unmannedinteroperability, network security, human-machine collaboration

• A Roadmap for US Robotics (presented to congress) includes focus onunmanned vehicles

– Highlights opportunities in transportation (people and goods), inspection, security and rescue,environmental monitoring

– Highlights needs in intelligent infrastructure, safe navigation and control algorithms, advancedsensors/perception, human/machine information sharing, robustness/security

• A Roadmap for US Robotics observes that the U.S. can only capitalize onadvancements in robotics and automation if instruction in robotics technologiesis broadly available at all levels of the education system, from K-12, vocational,undergraduate and graduate programs.

Funding Opportunities• DOD announced $4B a year to unmanned

systems across all DoD branches and offices• National Robotics Initiative 2.0: Ubiquitous

Collaborative Robots (multiple agencies under NSF lead)

• NSF announced AI Research Institute in 2019

Legal and Regulatory• Arguably behind the curve• Twenty-nine states, including Texas, have enacted legislation related to

autonomous vehicles• Texas is rather limited: allows for automated braking, allows use of autonomous

ground vehicles, defines owners and operators, preempts city legislation• Governors in eleven have issued executive orders related to autonomous vehicles.• National Highway and Transportation Safety Administration released federal

guidelines for Automated Driving Systems in 2016 and updated in 2019• U.S. House of Representatives has passed legislation and U.S. Senate has

legislation in committee

Goals of this Forum• Get to know faculty who are interested and better understand

UTA expertise• Identifying trends and future directions and sustain UTA

leadership• Identifying existing funding opportunities and support faculty

activity in securing them• Discuss what UTA can do to promote research in unmanned

vehicles • Form collaborative working groups to address national center-

type funding opportunities

Forum on Controls and Uses of Autonomous Vehicles

Frank L. Lewis, Ph.D., NAIMoncrief-O’Donnell Endowed Chair, UTA Research InstituteProfessor of Electrical Engineering, UTA

Talk available online at http://www.UTA.edu/UTARI/acs

Activities in Automatic Control, Multi-agent Game Theory, Autonomous Intelligent Vehicles

Supported by :US NSFONR, ARO, AFOSR

Yan Wan Nick Gans Ali DavoudiPatrik Kolaric Yusuf Kartal Victor Lopez

Moncrief-O’Donnell Chair, UTA Research Institute (UTARI)The University of Texas at Arlington, USA

with

F.L. Lewis National Academy of Inventors

UTA Research Institute Autonomous Systems Lab

Patrik KolaricYusuf KartalVictor Lopez

UTARI Autonomous Systems LabHuman/Autonomous Systems interactions: Interactive Control of multiple UAV / UGV Multiagent decision for autonomous driverless vehicles Autonomous navigation and motion planning for UAV / UGV Human Interfaces using voice control, gesture control Synchronization and collective control in multi-agent autonomous teams Neurocognitive Reinforcement Learning for autonomous control

UAV platforms-Parrot AR Drone quadcopters

DR Robot Jaguar 4x4

DR Robot V4

Human User Interfaces

Current funding of $1.5 M in ONR and NSF grantsUS Army Relevance. Decision and Control of Multi-Agent Teams. Received leveraging funding from US Army TARDEC, Ground Vehicle Robotics . Working with Dr. Dariusz Mikulski and Dr. Greg Hudas. Develop efficient learning mechanisms for military teams of humans and autonomous systems. Study Risk and Trust methods to develop coalitions in changing environments. Single User control of Multiple UAV/UGV.

US Navy Collaborations. Work with Brian Holm-Hansen at ONR, Gary Hewer at NAWS China Lake, Dr. Wei Kang at US Naval Postgraduate School in Monterey. Reinforcement Learning for Improved Control of Unmanned Aerial Vehicles. Autonomous Decision for Multi-body Intelligent Systems

US Air Force Relevance of Research in Coordination and Control of Multiple UAV Teams. Received leveraging funding from Kevin Bollino at AFOSR EAORD Europe. New methods for human coordination of multiple UAV systems. Trust-based learning mechanisms for improved performance and risk reduction of autonomous systems.

Dual-Use Tech Transfer to Industry:Current ContractsBoeing Defense Space & Security. Adaptive Controller for Phantom Ray Unmanned Aircraft.Various Companies. Bio-inspired Learning for Data-driven Industrial Process Control.Dual-Use Tech Transfer to Electric Power Microgrids:Multi-agent control of distributed renewable generation- Resilient distributed protocols for improved response of microgrids. Applications to Army Bases as microgrids.

Current Funding

1. F.L. Lewis and Yan Wan, “Fast Autonomous Vehicle Driving Decision based on Learning and Rule-basedCognitive Information,” Ford Contract for 3 years, $150K. April 2019-April 2022

2. F.L. Lewis and Yan Wan, “Heterogeneous Autonomous Sensor Networks for Optimizing Locomotion,” $50,000contract from Lockheed Martin Advanced Technology Labs, Feb.-Dec. 2019.

3. F.L. Lewis, Yan Wan, and Kyriakos Vamvoudakis, “Workshop on Distributed Reinforcement Learning andReinforcement Learning Games,” ARO grant, $30,000, April-June 2019.

4. F.L. Lewis, Yan Wan, and Ali Davoudi, EAGER: Real-Time: Collaborative Research: Unified Theory of Model-based and Data-driven Real-time Optimization and Control for Uncertain Networked Systems, NSF grant,$220,000, September 2018-August 2020.

5. F.L. Lewis and Yan Wan, “Optimal Design for Assured Performance of Interactive Multibody Systems,” ONRGrant, $815,000, June 2018-May 2022.

6. A. Davoudi, F.L. Lewis, and C. Edrington, “Distributed Autonomy, Resiliency, and Optimality in NavalMicrogrids,” ONR grant, $449,000, March 2017-March 2020.

Textbooks in Aircraft Control, Autonomous Systems, Machine Learning, Robotics, Multi-agent Systems

7 US Patents in Control Using Machine Reinforcement Learning, Multiagent Cooperative Control, Neural Adaptive Control, Intelligent Resource Assignment

400 journal papers

The Issues about Intelligent Feedback Control for Autonomous Vehicles

Automatic Feedback Control Systems must have provable stability, performance guarantees, and robustness

These are not required in computer science applications

MILSPEC Military Handling Qualities requirements for Aircraft Controllers

Commercial aircraft stability augmentation and autopilots need guarantees

Computer Science holds a wealth of Techniques such as Machine Learning, Game Theory, Nash equilibrium, Multi-agent decision, Deep Neural Networks

How can we use CS machine learning, multiagent decision and still guarantee stability ??

UAV Control applications at Boeing Defense Space & Security

Kevin Wise and Eugene Lavretsky

Highly reliable adaptive uncertainty approximation compensators for flight control applications:

unmanned aircraft – Phantom Ray

UAV Applications of our Machine Learning Neural Adaptive Control Technology

Our Neural Adaptive Controller is Currently Flown on Boeing Phantom Ray Unmanned Aircraft

Patent- R. Selmic, F.L. Lewis, A.J. Calise, and M.B. McFarland,"Backlash Compensation Using Neural Network," U.S. Patent6,611,823, awarded 26 Aug. 2003.

Dynamics and Control of Quadrotor UAV

AR Drone Parrot

Crazyflie

3D Robotics Octocopter

Rotorcraft UAV

Angular position –attitudes

Position –navigational states

The Quadrotor States

Roll

Pitch

yaw

xyz

Xφθψ

=

Body Axes Vs. Earth-fixed axesQuadrotor UAV Control Framework

ϕ

θ

ψ

τ

τ ττ

=

Lift

torques

u

Only 4 control inputs

Control Problem - An Underactuated System

Position subsystem

Attitude subsystem

Quadrotor Dynamics consists of TWO COUPLED Lagrange Dynamical Systems

New Backstepping Control Design Technique

Control Problem –Two interacting dynamical systemsAn Underactuated System

6 states but only 4 control inputs

Neural Adaptive Backstepping Flight Controller- 2 Control loops

Attitude Control Inner LoopPosition Control Outer Loop

Machine Learning Neural Adaptive Compensator for Dynamic Effects

Control Allocation Unit

Problem- built into the UAV. Can only implement PID controller

Problem- Inner attitude control loop is built into the UAV Can only implement PID controller

Reinforcement Learning

Every living organism improves its control actions based on rewards received from the environment

The resources available to living organisms are usually meager. Nature uses optimal control.

1. Apply a control. Evaluate the benefit of that control.2. Improve the control policy.

RL finds optimal policies by evaluating the effects of suboptimal policies

Optimality in Biological Systems

Optimality Provides an Organizational Principle for Behavior

Charles Darwin showed that Optimal Control over long timescales. Is responsible for Natural Selection of Species

Why are Biological Systems Resilient?

Cell Homeostasis The individual cell is a complex feedback control system. It pumps ions across the cell membrane to maintain homeostatis, and has only limited energy to do so.

Cellular Metabolism

Permeability control of the cell membranehttp://www.accessexcellence.org/RC/VL/GG/index.html

Optimality in Biological SystemsWhy are Biological Systems Resilient?

Multi-player Game SolutionsIEEE Control Systems Magazine, Dec 2017

K. Vamvoudakis, D. Vrabie, and F.L. Lewis, “Controlmethodology for online adaptation to optimal feedbackcontroller using integral reinforcement learning,”US patent 9,134,707 issued 15 Sept. 2015.

Bring Machine Learning into Feedback Control - Reinforcement Learning for ImprovedOptimal Adaptive Control

DDO – Online Data-Driven Optimization

Work with Dan Levine in Neurocognitive Psychology for Controls

Doya, Kimura, Kawato 2001

Limbic system

Motor control 200 Hz

theta rhythms 4-10 HzDeliberativeevaluation

control

Reinforcement Learning Optimal Adaptive ControllerDraguna Vrabie

Actor-Critic Machine Learning StructureFor Continuous-time Systems

Standard Adaptive Controller

Supervisory Reinforcement Learning Controller

Optimal Feedback Control-Minimum ResourcesMinimum fuelMinimum timeMaximum efficiency

Charles Darwin showed that Nature Uses Reinforcement Learning Optimal Control for the Evolution of Species. The Resources available to natural biological systems are usually meager

K. Vamvoudakis, D. Vrabie, and F.L. Lewis, “Control methodology for online adaptation to optimal feedback controller usingintegral reinforcement learning,” US patent 9,134,707 issued 15 Sept. 2015.

xu

V

ZOH T

x Ax Bu= +System

T Tx Qx u Ruρ = +

Critic

ActorK−

T T

Optimal engine controllers based on RL for Caterpillar, Ford (Zetec engine), and GM

Applications of Our Algorithms to Auto Engine Control

Student S. Jagannathan, NAI17 US patents

8-10% improvement in fuel efficiency and a drastic reduction in NOx (90%), HC (30%) and CO (15%) by operating with adaptive exhaust gas recirculation.

Savings to Caterpillar were over $65,000 per component.

S. Jagannathan and F.L. Lewis, "Discrete-time tuning of neural network controllers for nonlinear dynamical systems," U.S.Patent 6,064,997, awarded 16 May 2000.

F.L. Lewis, H. Zhang, A. Das, K. Hengster-Movric, Cooperative Control of Multi-Agent Systems: Optimal Design and Adaptive Control, Springer-Verlag, 2013

Multiple Interacting UAVSwarmsFormations

Need a Distributed Decision &Control Strategy

Multi-Agent Interacting Systems

Shan Zuo, Y.D. Song, F.L. Lewis, and A. Davoudi,, “Time-Varying OutputFormation-Containment of General Linear Homogeneous and HeterogeneousMulti-Agent Systems,” IEEE Transactions on Control of Network Systems, vol.6, no. 2, pp. 537-548, June 2019.

Ci Chen, Kan Xie, Frank L. Lewis, Shengli Xie, and Ali Davoudi, “Fully Distributed Resilience for Adaptive Exponential Synchronization of Heterogenous Multi-Agent Systems Against Actuator Faults,” IEEE Trans. Automatic Control, vol. 64, no. 8, pp. 3347-3354, Aug. 2019

Leader or root node

Followers

Communication Graph

Formation of Multiple UAV

Multiple Interacting Autonomous Vehicles

Interacting Dynamical Systems

Desired Formation Topology

Leader

Cyber/physical System - The way we communicate can either Limit or Enhance the Way we interact. Think of the Internet

Each agent only sees its local neighbors

Synchronization of Multiple Quadrotor UAV

Single User controls Formation of 3 UAV Quadrotors

Submarine Control Surfaces

Unmanned Underwater Vehicles Robust Distributed Formation Controller

Kinematic Equation

Dynamics Motion Equation

UUV Dynamics consists of TWO COUPLED Lagrange Dynamical Systems

Hao Liu, Yanhu Wang, and Frank L. Lewis, “Robust Distributed Formation ControllerDesign for a Group of Unmanned Underwater Vehicles,” IEEE Transactions on Systems,Man, and Cybernetics: Systems, to appear 2019.

1. Victor G. Lopez, F.L. Lewis, Yan Wan, Edgar N. Sanchez, and Lingling Fan, “Solutions for Multiagent Pursuit-Evasion Games onCommunication Graphs: Finite-Time Capture and Asymptotic Behaviors,” IEEE Transactions on Automatic Control, to appear,2019.

2. Hao Liu, Yu Tian, F.L Lewis, Yan Wan, K.P. Valavanis, “Robust Formation Tracking Control for Multiple Quadrotors underAggressive Maneuvers, Automatica, to appear 2019.

3. Shan Zuo, Y.D. Song, F.L. Lewis, and A. Davoudi,, “Time-Varying Output Formation-Containment of General LinearHomogeneous and Heterogeneous Multi-Agent Systems,” IEEE Transactions on Control of Network Systems, vol. 6, no. 2, pp.537-548, June 2019.

4. Kan Xie, Ci Chen, Frank L. Lewis, and Shengli Xie, “Adaptive Compensation for Nonlinear Time-varying Multi-Agent Systems withActuator Failures and Unknown Control Directions,” IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1780-1790, May 2019.

5. H. Modares, Bahare Kiumarsi, F.L. Lewis, F. Ferrese, and Ali Davoudi, “Resilient and Robust Synchronization of Multi-agentSystems Under Attacks on Sensors and Actuators,” IEEE Transactions on Cybernetics, to appear 2019.

6. Victor Lopez and F.L. Lewis, “Dynamic Multiobjective Control for Continuous-time Systems using Reinforcement Learning,” IEEETrans. Automatic Control, vol. 64, no. 7, pp. 2869-2874, July 2019.

7. Jinna Li, Tianyou Chai, F.L. Lewis, Jinliang Ding, Yi Jiang, “Off-policy interleaved Q-learning: optimal control for affine nonlineardiscrete-time systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 5, pp. 1308-1320, May 2019.

8. Bahare Kiumarsi, K. Vamvoudakis, H. Modares, and F.L. Lewis, “Optimal and Autonomous Control Using ReinforcementLearning: A Survey,” IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2042-2061, June 2018.

A few of our Recent Papers in Autonomous Vehicles and Multi-agent Intelligent Systems

Two opposing teams of MAS in Graphical Zero-Sum games. Protagonists(blue) try to minimize a value function, whereas antagonists (orange) want tomaximize it. The result is a local Nash saddle-point equilibrium at each nodeand also global Nash between the two teams.

Opposing Teams and Malicious Adversaries’ on Networks

We study many sorts of Autonomous Vehicle and Social Behaviors in communication networks

0kzAdversarial Leader Group

ikh

ikg0kx

Leader Group

Social Network with weak links

Disturbance inputs of social agents are subverted by adversarial leaders to mount a coordinatedattack on a social network. The social net has cut-sets of weak links (shown dashed in red), andso has the potential to be fractured into disconnected subgroups.

Infiltration of Weakly Linked Splinter Groups in Social Networks

Evader set V

Pursuer Set U

Multiple evaders (blue) are networked by interaction graph Ge. Multiple pursuers (red) are networkedby graph Gp. Pursuer and evader interactions are captured by bipartite graph Gep (dashed edges).

Multi-agent Pursuit-Evasion Games

To appear in IEEE Transactions on Automatic Control

Work of Victor Lopez

Real-time Multiplayer Games for Autonomous Traffic Decision Ford Contract –Dimitar Filev and Subramanya Naeshrao

Each agent has dynamics- e.g. moving vehiclesEach agent tries to optimize its own performance function

Applications of multiagent systems in autonomous vehicle systemsLane changing problemIntersection ProblemFreeway merging problemPlatoon convoysChanging platoons

Work of Victor Lopez

RL for Human-Robot Interaction (HRI)

1. H. Modares, I. Ranatunga, F.L. Lewis, and D.O. Popa, “Optimized Assistive Human-robot Interaction using Reinforcement Learning,” IEEETransactions on Cybernetics, vol. 46, no. 3, pp. 655-667, 2016.

2. I. Ranatunga, F.L. Lewis, D.O. Popa, and S.M. Tousif, "Adaptive Admittance Control for Human-Robot Interaction Using Model ReferenceDesign and Adaptive Inverse Filtering" IEEE Transactions on Control Systems Technology, vol. 25, no. 1, pp. 278-285, Jan. 2017.

3. B. AlQaudi, H. Modares, I. Ranatunga, S.M. Tousif, F.L. Lewis, and D.O. Popa, “Model reference adaptive impedance control for physical humanrobot interaction,” Control Theory and Technology, vol. 14, no. 1, pp. 1-15, Feb. 2016.

PR2 meets Isura

Forum on Controls and Uses of Autonomous Vehicles

Yan Wan, Ph.D.Associate Professor, Electrical Engineering

Urban Aerial Mobility: The Cyber-Physical Systems Approach

IoT and Smart City Applications of UAVs

Sample Effort 1 from My Group: Smart Emergency Response System (SERS)

• Smart Emergency Response System, our collaborative efforts with Boeing, MathWorks, National Instruments, NCSU, U of Washington, WPI, and MIT Lincoln lab, 2013-2017https://www.youtube.com/watch?v=Yi_dK4iRCA4&t=34s

Sample Effort 2 from My Group: ARFL Search and AI Challenge

AFRL Search and AI Challenge, 1st place in one competition run, and among the only two teams who scored top five in all competition runs, April 2019

https://www.youtube.com/watch?v=fOq56R7DxDk

Currently Active Projects• Nine currently active external projects for a total of over $3M,

including five from NSF, and four from other agencies and industries.

• NSF: CPS Transition to Practice: Supplement to CAREER: Co-Design of Networking and Decentralized Control to Enable Aerial Networks in an Uncertain Airspace, $147,240, 08/30/2019-08/31/2021, Sole PI (100%).

• Ford: Fast Autonomous Driving Decision based on Learning and Rule-based Cognitive Information, $150,000, 2019-2022, Co-PI (50%), collaboration with Frank Lewis (PI).

• ONR: Optimal Design for Assured Performance of Interactive Multibody Systems: Guaranteed Controls for Multi-pursuers, Estimation, Optimal Learning, Scalable Uncertainty Sampling, and Time-critical Communication, $815,019, 06/01/2018-05/31/2022, Co-PI (50%), collaboration with Frank Lewis (PI).

• NSF: Real-Time: Collaborative Research: Unified Theory of Model-based and Data-driven Real-time Optimization and Control for Uncertain Networked Systems, $220,010 (project total $300,000), 09/15/2018-08/31/2020, Co-PI (33%), collaboration with Frank Lewis (PI) and Ali Davoudi (Co-PI), and TAMU-CC.

• NSF CI-New: Collaborative Research: Developing an Open Networked Airborne Computing Platform, $289,714 (project total: $998,803), 09/01/2017 - 08/31/2020, Lead Institution PI (100%), collaboration with TAMU-CC, UNT, and UPRM.

• NSF S&AS: FND: Safe Task-Aware Autonomous Resilient Systems (STAARS), $549,836, 09/01/2017-08/31/2020, Co-PI (20%), collaboration with Atilla Dogan (PI), Kamesh Subbarao, Manfred Huber, and Brian Huff.

• NSF CAREER: Co-design of Networking and Decentralized Control to Enable Aerial Networks in an Uncertain Airspace, $442,538, 06/01/2015-05/31/2020, Sole PI (100%).

• ARO: Workshop on Distributed Reinforcement Learning and Reinforcement Learning Games, $30,000, 04/01/2019-09/31/2019, Co-PI (50%), collaboration with Frank Lewis (PI).

• Lockheed: Heterogeneous Autonomous Networks for Sensor Optimizing Locomotion, $50,000, 02/15/2019 - 10/27/2019, Co-PI (50%), collaboration with Frank Lewis (PI).

Current Interest: Ensure Safety and EfficiencyUrban Aerial Mobility (UAM) & UAV Traffic Management (UTM)

• Traditional air traffic management (ATM) is concerned with multiple spatiotemporal scales:

• Individual aircraft GNC• Air traffic control • air traffic flow management• Airspace management

• UAV traffic much more complicated• More uncertainty• More heterogeneity• More diverse service providers

Our Cyber-Physical Systems Approach to UAM and UTM

1. Communication and Control Co-design for Long-distance and Broad-band UAV Networking

• UAVs to provide long-distance broad-band on-demand emergencycommunication

• The control of directional antennas facilitates communication• Received signal strength, the communication indicator, serves as

measurement and goal function for control• Communication measurement data learns the environmental-specific

communication model, and distributed reinforcement learning is used for adaptive optimal control.

• Flight tests, water-proof design, and user-friendly interface design for technology transfer in the safety-critical emergency response application.

2. UAV Weather Service and On/Off-board Sensing

3. Multiple-Vehicle Coordination: Differential Graphical Games

4. UAV Airspace Capacity and Its Connection to Local Autonomy

5. Contingency Management and Multi-UAV Path Planning

Integration of Theory, Practice, and Community Engagement• Invited talks at NSF, Southeast Tarrant Transportation (SETT) Partnership Breakfast, NCTCOG,

Mathworks, and many other universities and industries• Closely work with local agencies and industries• Organized a number of workshops at international conference and UTA• Many outreach activities to community• Work with Bell on the STEM Competition

Forum on Controls and Uses of Autonomous Vehicles

Animesh Chakravarthy, Ph.D.Associate Professor, Mechanical and Aerospace Engineering

Forum on Controls and Uses of Autonomous Vehicles

Nicholas Gans, Ph.D.Division Head - Automation and Intelligent Systems, UTARI

Automation and Intelligent Systems at The UT Arlington Research Institute

Advancing Intelligent Systems TRLs Through Federal Funded R&Dat UT Arlington Research Institute

• The AIS Division at UTARI is dedicated to developing robotics and automation tools to with direct application to real-world problems.

• Funding is from a wide range of agencies and companies

• Unmanned Vehicles• Augmented Reality• Control Algorithms

• Swarms of networked vehicles for surveillance

• Natural Interfaces to ease physical and cognitive load

• Machine Learning for target detection and recognition

• Bespoke Automation Solutions• Prototype development • Testing and

• Control and navigation of custom swarm sir vehicles

• In-home medication delivery and monitoring

• Advanced manufacturing• Inventory inspection and sorting

• Personal robots for assistive care and social interaction

• Unmanned inspection of roads and infrastructure

• Assistive care for elderly and disabled

• Next generation powered prosthetics

• Experiential education

Industry SocietyDefense

Sect

orDo

mai

nsAp

plic

atio

ns

• Four-year effort sponsored by the Office of Naval Research to create optimal designs for assured performance of interactive, multi-UAV systems

• Three-year National Science Foundation funded collaboration to enable the use of networked UAVs for civilian applications such as intelligent transportation, emergency response, infrastructure monitoring and agriculture

• Multiple two-year project sponsored by Air Force Research Laboratory on distributed search in urban environments & vision-based distributed formation control

Research in Multi-agent, Multi-system Autonomy funded byDoD Research Labs and Offices

• Lockheed Advanced Technology Labs on control of Heterogeneous UVS swarms (air/ground/water) and Manned, Unmanned Teaming utilizing AI/ML and AR

• Small Business Newcastle Manufacturing - UAV teams lighting remote areas for first responders

Applied Unmanned Systems Developments Through Industry Partnership at UTA Research Institute

• DOD Air Force STTR Phase I with small business for High Speed High Accuracy Artificial Neural Networks for UAV-based Identification of UAVs – STTR Phase II invited and submitted

UAVs for Surveying, Inspection and Environmental Monitoring

• National Science Foundation (NSF) RAPID award for remote surveys of debris in Houston after Hurricane Harvey in 2017

• Fusion of visible light and hyperspectral cameras for use onboard UAVs• Texas Department of Transportation funded projects:

1. Project using UAVs for performing bridge inspections2. Project using UAVs for roadway inspections

UAV Flying Underneath Bridge