Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles

Abstract

As the need for autonomous reconnaissance and surveillance missions in cluttered urban environments has been increasing, this paper describes a cooperative path planning algorithm for tracking a moving target in urban environments using both unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). The novelty of the algorithm is that it takes into account vision occlusions due to obstacles in the environment. The algorithm uses a dynamic occupancy grid to model the target state, which is updated by sensor measurements using a Bayesian filter. Based on the current and predicted target behavior, the path planning algorithm for a single vehicle (UAV/UGV) is first designed to maximize the sum of the probability of detection over a finite look-ahead horizon. The algorithm is then extended to multiple vehicle collaboration scenarios, where a decentralized planning algorithm relying on an auction scheme is designed to plan finite look-ahead paths that maximize the sum of the joint probability of detection over all vehicles.