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

TL;DR

This paper introduces a distributed ADMM-based approach for non-myopic multi-target tracking that improves target estimation by coordinating sensor paths without heuristic target assignment.

Contribution

It reformulates non-myopic path planning as a distributed optimization problem using ADMM, enabling automatic consensus and improved real-time multi-target tracking.

Findings

Enhanced target tracking accuracy in simulations

Efficient real-time operation with modified RHC and edge-cutting

Automatic high-level decision making among targets

Abstract

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.