Abstract-In this paper we study a non-cooperative zerosum game where one player performs reconnaissance while the second player constantly observes the first. This game has implications for teams of UAVs operating within aural and visual detection range of threat forces. In particular, the threat can potentially react dynamically to UAV observations and endanger future movements. We propose a specific behavior essential to an optimal policy for a team of agents, and create a randomized algorithm inspired by these heuristics. Implementation of this path planner onto a team of autonomous helicopters demonstrated the utility of the algorithm in real time applications.

Abstract-Coordinated navigation by two cooperating sensor-equipped agents in a partially known static environment is investigated. Each agent observes a local part of the otherwise unknown environment and shares the gathered data with the other agents. In general, dynamic programming techniques suitably model the navigation problem, but are computationally hard to solve. We propose a combined dynamic and linear programming framework to circumvent the curse of dimensionality and establish in the process a firm upper bound on the spatial separation of a two-agent cluster navigating on a structured arbitrarily large graph.

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