Adaptive Cooperative Path and Mission Planning for Multiple Aerial Platforms

In the phase 1 effort, IAI and its subcontractor, Prof. Jose B. Cruz of the Ohio State University (OSU), have successfully developed and demonstrated innovative algorithms and software tools that are built on a hierarchical game-theoretic framework to automate path planning and mission planning for multiple unmanned platforms to enhance system effectiveness and robustness. We have provided a general framework for the design of a hierarchical cooperative plan architecture that exploits the advantages of a sensor network onboard the UAVs by combining both collaborative decision making (coordination) and decentralized real time control. With this novel structure and the incorporation of game theoretic framework for estimation and control, the UAV operations from this architecture can optimally track multiple targets and adapt to the dynamic and uncertain environment. In Phase II, we will refine and expand the path and mission planning algorithms. First, we will develop a cooperative target tracking and data fusion algorithm. Second, we will develop an emitter localization algorithm. Third, investigate extension of current planning algorithm. Forth, investigate extension of current game theory, whereby players act sequentially rather than simultaneously. Fifth, develop a software prototype for field test.