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This paper investigates non-myopic path planning of mobile sensors formulti-target tracking. Such problem has posed a high computational complexityissue and/or the necessity of high-level decision making. Existing works tacklethese issues by heuristically assigning targets to each sensing agent andsolving the split problem for each agent. However, such heuristic methodsreduce the target estimation performance in the absence of considering thechanges of target state estimation along time. In this work, we detour thetask-assignment problem by reformulating the general non-myopic planningproblem to a distributed optimization problem with respect to targets. Bycombining alternating direction method of multipliers (ADMM) and localtrajectory optimization method, we solve the problem and induce consensus(i.e., high-level decisions) automatically among the targets. In addition, wepropose a modified receding-horizon control (RHC) scheme and edge-cuttingmethod for efficient real-time operation. The proposed algorithm is validatedthrough simulations in various scenarios.

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking