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In this article, we consider a multi-agent path planning problem in astochastic environment. The environment, which can be an urban road network, isrepresented by a graph where the travel time for selected road segments(impeded edges) is a random variable because of traffic congestion. An unmannedground vehicle (UGV) wishes to travel from a starting location to a destinationwhile minimizing the arrival time at the destination. UGV can traverse throughan impeded edge but the true travel time is only realized at the end of thatedge. This implies that the UGV can potentially get stuck in an impeded edgewith high travel time. A support vehicle, such as an unmanned aerial vehicle(UAV) is simultaneously deployed from its starting position to assist the UGVby inspecting and realizing the true cost of impeded edges. With the updatedinformation from UAV, UGV can efficiently reroute its path to the destination.The UGV does not wait at any time until it reaches the destination. The UAV ispermitted to terminate its path at any vertex. The goal is then to develop anonline algorithm to determine efficient paths for the UGV and the UAV based onthe current information so that the UGV reaches the destination in minimumtime. We refer to this problem as Stochastic Assisted Path Planning (SAPP). Wepresent Dynamic $k$-Shortest Path Planning (D*KSPP) algorithm for the UGVplanning and Rural Postman Problem (RPP) formulation for the UAV planning. Dueto the scalability challenges of RPP, we also present a heuristic basedPriority Assignment Algorithm (PAA) for the UAV planning. Computational resultsare presented to corroborate the effectiveness of the proposed algorithm tosolve SAPP.

Assisted Path Planning for a UGV-UAV Team Through a Stochastic Network