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

UW Computer Science Department Scenario Terrain detector Target detector Separation radius Maximize area coverage and probability of detection of the targets by the ensemble.

UW Computer Science Department 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.

UW Computer Science Department 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)

UW Computer Science Department 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.

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

UW Computer Science Department 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 =  2 –Terrain sensor coverage =  2 Surveillance over an area L 2 > M  2 >> M  2 GOAL: Maximize number of tanks detected that have been visible at some point in time.

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

UW Computer Science Department 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.

UW Computer Science Department Stationary Targets Both approaches work quite well when targets are stationary

UW Computer Science Department Gollum Targets LJ holds up better when targets are Gollum controlled

UW Computer Science Department Super-Gollum Targets LJ holds up better when targets are Super-Gollum controlled

UW Computer Science Department Super-Gollum Sensitivity LJ is robust to increasing/decreasing number of assets

UW Computer Science Department Super-Gollum Sensitivity LJ holds up well when target detection probability lowered

UW Computer Science Department 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.

UW Computer Science Department 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.

UW Computer Science Department Gollum Targets LJM places assets over open areas more often, improving performance