Potential game based cooperative control in dynamic environments

Abstract

The objectives of this research are to extend cooperative control methods based on potential games to dynamic environments and to develop an experimental test bed to illustrate theoretical results. Cooperative control concerns coordinating a collective performance of multiple autonomous agents. Possible applications include mobile sensor networks, distributed computation, and unmanned vehicle teams. Prior work has explored game theory, specifically the framework of potential games, as an approach to cooperative control, but has been restricted to static environments. This research shows that potential game based cooperative control also can be applied to dynamic environment problems. The approach is illustrated on three example problems. The first one is a moving target tracking problem using a modified form of the learning algorithm, restrictive log-linear learning. The second example is mobile sensor coverage for an unknown dynamic environment. The last example is multi-agent path optimization using payoff based learning. The performances of the developed systems are studied by simulation. The last part of this thesis develops an experimental moving target tracking system using multiple mobile robots. Finally, the thesis concludes with suggestions for future research directions.