Probabilistic path planning for cooperative target tracking using aerial and ground vehicles

In this paper, we present a probabilistic path planning algorithm for tracking a moving ground target in urban environments using UAVs in cooperation with UGVs. The algorithm takes into account vision occlusions due to obstacles in the environments. The target state is modeled using the dynamic occupancy grid and the probability of the target location is updated using Bayesian Altering. Based on the probability of the target's current and predicted locations, the path planning algorithm is designed to generate paths for a single UAV or UGV maximizing the sum of probability of detection over a finite look-ahead. For target tracking using multiple vehicle collaboration, a decentralized planning algorithm using an auction scheme generates paths maximizing the sum of joint probability of detection over the finite look ahead horizon. Simulation results show the proposed algorithm is successful in solving the target tracking problem in urban environments.

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