Hybrid and Switched System Approaches to Integrated Modeling and Control of Mixed Human and Robot Teams

1. Hybrid and Switched System Approaches to Integrated Modeling and Control of Mixed Human and Robot Teams Kristi A. Morgansen Linh Vu Department of Aeronautics and Astronautics University of Washington

2. 2 Outline Research overview Motivating Scenarios Switched Systems Hybrid Models for TAFC Tasks Ongoing and future directions

3. What is COUNTER? Concept Cooperative Small and Micro UAVs Heterogeneous team for target location and ID Small UAVs ~ High Altitude Big Picture Target Nomination Search Mission Multiple Micro UAV ~ Low Autonomous Sensor Placement Details and ID at lower altitudes The COUNTER concept The COUNTER concept

4. Object Allocation Algorithm Plot shows 4 Micro UAS tours (red, blue, green & gold). All vehicles start at the large green dot. Targets are red dots numbered 1 thru 20. Objective is to visit all of the targets while maintaining the maximum possible reserve capacity on each of the MAVs. In order to do this, the algorithm solves two problems. First, it decides which UAS needs to find which set of targets; and then which path each UAS needs to take in order to find these targets. The algorithms needs to do this in minimum time following minimum path. This is necessary since micro air vehicles have only flight time of approximately 25 minutes and we need all these vehicles to have reserve capacity to carry on other tasks for the mission. This algorithm immediately provides a good feasible solution. This makes the algorithm a good candidate for implementation in realistic systems. The quality of the solution monotonically improves over algorithm run-time until the optimal solution is reached. Referring to the block diagram on the previous chart, we have completed the work corresponding to the block �MAV object allocation.� We are now in the process of taking human operator inputs into consideration in order for MAVs to decide their plan of action. For example, MAVs may go take another look at an object, or they may decide to fly around an object to get 360 degree view, etc. This work is represented by the block �MAV control design.� It is necessary to note that we are addressing long term basic research needs while we use spin offs of our research results to solve near term problems. Our 6.1 work is playing a critical role in our important 6.2 program. Our 6.1 research will continue after the 6.2 program for example, by extending our resource allocation research to address more complex battle scenarios such as pop up threats, decoys, false information in the network etc�..Plot shows 4 Micro UAS tours (red, blue, green & gold). All vehicles start at the large green dot. Targets are red dots numbered 1 thru 20. Objective is to visit all of the targets while maintaining the maximum possible reserve capacity on each of the MAVs. In order to do this, the algorithm solves two problems. First, it decides which UAS needs to find which set of targets; and then which path each UAS needs to take in order to find these targets. The algorithms needs to do this in minimum time following minimum path. This is necessary since micro air vehicles have only flight time of approximately 25 minutes and we need all these vehicles to have reserve capacity to carry on other tasks for the mission. This algorithm immediately provides a good feasible solution. This makes the algorithm a good candidate for implementation in realistic systems. The quality of the solution monotonically improves over algorithm run-time until the optimal solution is reached. Referring to the block diagram on the previous chart, we have completed the work corresponding to the block �MAV object allocation.� We are now in the process of taking human operator inputs into consideration in order for MAVs to decide their plan of action. For example, MAVs may go take another look at an object, or they may decide to fly around an object to get 360 degree view, etc. This work is represented by the block �MAV control design.� It is necessary to note that we are addressing long term basic research needs while we use spin offs of our research results to solve near term problems. Our 6.1 work is playing a critical role in our important 6.2 program. Our 6.1 research will continue after the 6.2 program for example, by extending our resource allocation research to address more complex battle scenarios such as pop up threats, decoys, false information in the network etc�..

6. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics.

7. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics.

8. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics. Low Fidelity visual to simulate out the window camera. Actually used to refine algorithms than graphics.

9. Switched Systems 9

10. Two Alternative Forced Choice Tasks 10

11. Two Alternative Forced Choice Tasks 11

12. Two Alternative Forced Choice Tasks 12

13. Two Alternative Forced Choice Tasks 13

14. Two Alternative Forced Choice Tasks 14

15. Two Alternative Forced Choice Tasks 15

16. Two Alternative Forced Choice Tasks 16

17. Two Alternative Forced Choice Tasks 17

18. Two Alternative Forced Choice Tasks 18

19. Two Alternative Forced Choice Tasks 19

20. Two Alternative Forced Choice Tasks 20

21. Two Alternative Forced Choice Tasks 21

22. Summary and Future Work 22