UAV Autonomy. Dr. Derek Kingston Control Science Center of Excellence Aerospace Systems Directorate Air Force Research Laboratory

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1 UAV Autonomy 19 Sept 2012 Dr. Derek Kingston Control Science Center of Excellence Aerospace Systems Directorate Integrity Service Excellence Air Force Research Laboratory 1

2 Outline Relevance to Air Force Context for MACCCS work Collaborations Ultimate goals 2

3 AF Relevance Autonomous, cooperative UAVs fill critical AF need Future Games Volume fight, no time to micromanage UAVs Contested, t large-scale l environments Robust to loss of communication Tech Horizons: Flexibly autonomous systems are critical to the future of the Air Force 3

4 MACCCS At Every Level of Control Cooperative Control Autonomous Control Loops Mission Planner Task Execution LTL, Process Algebra Road Surveillance Intruder Estimation Human Operator Outer Loop Trajectory RRT* Traditional Control Loops Inner Loop Stability Air Vehicle Hypersonic Vehicles Flapping wing MAV ( pre 2011) 4

5 MACCCS Players UAV Cooperative Control University of Michigan: Prof. Anouck Girard (PI & Director) Prof Pierre Kabamba Massachusetts Institute of Technology Prof. Emilio Frazzoli Micro Air Vehicle Control Purdue University: Prof. Xinyan Deng Note on Human Factors Upon recommendation dti from board, no longer pursuing under MACCCS effort Relying on AFRL/RH to provide human factors considerations 5

6 Recent Interactions Active visiting summer research program Jonathon Las Fargeas (Girard) Two-week week summer collaborations Maj Phil Root (Frazzoli) Kyle Treleaven (Frazzoli) Noele Norris (Frazzoli) Delivery of RRT* trajectory planner for Dubins vehicles to AFRL (Karaman, Frazzoli) 6

7 Goals Cooperative autonomous decision making for teams of UAVs in surveillance and tracking scenarios Understand interplay of trajectory planning and mission i planning layers Increase consideration of operation in adversarial environments (lack of communication, active adversary) 7

8 Ground Sensor Networks Unattended Ground Sensors (UGS) provide valuable situational awareness, however: Can be difficult to emplace Limited by line-of-sight to communication node Comm node must have reliable power source Frequent false alarms Demonstrate application of realistic decentralized cooperative control 8

9 UAV/UGS System Considerations Main driver of UGS system is communication network. Replacing gateway node with UAV team: Alleviates need for careful LOS placement Ability to place larger numbers of UGSs No reliance on permanent power source Automatically reject some false alarms Gather airborne imagery of potential intruders Tradeoffs: reporting frequency, UAV logistics (airspace, maintenance, etc) 9

10 Scenario Phases Patrol: optimal revisit to all UGS based on a priori likelihood of intrusion Isolation: determine with high confidence the location of the intruder and obtain aerial imagery of the intruder Delivery: cue sensor operator to video or vector UAV to delivery region Response: operator takes action based on UGS history and collected imagery 10

11 Valid Intruder Path UAV Communication Range UGS Sensor Range UGS Communication Range BASE 11

12 UGS Sensor Range UAV Communication Range UGS Communication Range BASE 12

13 Time (A) 13

14 Time (B) 14

15 Time (C) 15

16 Time (D) 16

17 Extensions Cooperative algorithms for intruder capture Guarantees for capture on simplified graphs Strategies for placement of UGS Delayed message passing to facilitate coordination (pheromone-like trails) Multiple intruders and corrupt sensors Game-theoretic considerations 17

18 Conclusions MACCCS research efforts address increasing autonomy for Air Force missions Communication realities must be addressed by autonomous decision algorithms Cooperative UAV/UGS problem presented to highlight region in which actionable information can be gathered by the system 18

19 DISTRIBUTION STATEMENT A Unclassified, Unlimited Distribution 19