Optimizing Interaction Potentials for Multi-Agent Surveillance

1. Optimizing Interaction Potentials for Multi-Agent Surveillance Dr. William M. Spears Dr. Diana F. Spears Wesley Kerr Suranga Hettiarachchi Dimitri Zarzhitsky University of Wyoming

2. Scenario Separation radius Separation radius Target detector Terrain detector Maximize area coverage and probability of detection of the targets by the ensemble.

3. Asset Control • Control the motion of assets via interactions with neighboring assets. • Interactions are determined via physics-based potentials (F = ma simulation). This is called “artificial physics” or AP. • Optimize potentials to achieve the best performance using genetic algorithms.

4. Class of Potentials Examined • We evolved asset-asset forces of two forms: • Also, a viscous friction term is evolved, which ranges from no friction to full friction (1.0 to 0.0)

5. Target Control • Stationary targets for baseline study. • Gollum target controller: • Targets try to cross from left to right through environment while sneaking through foliage. • Super-Gollum target controller: • Also tries to avoid the UAV sensor footprint.

6. Architecture Environment Generator Create environments Particular interaction potential Run F=ma simulator with particular interaction potential. Measure performance w/n environments Genetic Algorithm evolving population of interaction potentials Return performance to GA Output best interaction potential if desired performance met or time elapsed.

7. Example Scenario • 5-20 Micro-Air Vehicles (assets) at constant altitude. • Environment size = 200x200 with some % foliage. • Targets of interest: 100 tanks. • Sensors have a fixed field of view. • Target sensor coverage = p52 • Terrain sensor coverage = p202 • Surveillance over an area L2 > M p202 >> M p52 • GOAL: Maximize number of tanks detected that have been visible at some point in time.

8. Environment Generator forest Separation radius tank Foliage field of view MAV Target field of view Note: separation radius can depend on foliage Only 3 MAVs shown here

9. Experimental Comparison • Methods: • We compare the evolved AP force laws with the evolved LJ force laws (using 10 assets) against: • Stationary targets • Gollum targets • Super-Gollum targets • Sensitivity analyses are also measured with respect to the number of assets and the fidelity of the target detector.

10. Stationary Targets Both approaches work quite well when targets are stationary

11. Gollum Targets LJ holds up better when targets are Gollum controlled

12. Super-Gollum Targets LJ holds up better when targets are Super-Gollum controlled

13. Super-Gollum Sensitivity LJ is robust to increasing/decreasing number of assets

14. Super-Gollum Sensitivity LJ holds up well when target detection probability lowered

15. Conclusions • In general the LJ potential outperformed the AP potential. • The evolved potential is robust with respect to the loss of one half of the assets or sensor degradation. • The evolved potential is robust to changes in the percentage of foliage. • This robustness emerges despite the fact that the evolved potential was not explicitly trained for these degradations.

16. Extensions • Currently extending LJ to include a virtual mass term. If asset is over open area mass increases, and velocity decreases (obeying conservation of momentum). • Also examining sweeping behavior controlled via kinetic theory.

17. Gollum Targets LJM places assets over open areas more often, improving performance