Multi-Objective Multi-Agent Planning for Jointly Discovering and Tracking Mobile Object

TL;DR

This paper introduces a novel multi-objective POMDP framework for multi-agent systems to efficiently discover and track mobile objects with limited sensors, addressing detection uncertainties and conflicting goals.

Contribution

It formulates a new multi-objective POMDP based on information theory and proves a greedy algorithm's approximation guarantee for multi-agent control.

Findings

The proposed value function is monotone submodular.

The greedy algorithm achieves a (1-1/e) approximation.

Effective online planning for joint discovery and tracking.

Abstract

We consider the challenging problem of online planning for a team of agents to autonomously search and track a time-varying number of mobile objects under the practical constraint of detection range limited onboard sensors. A standard POMDP with a value function that either encourages discovery or accurate tracking of mobile objects is inadequate to simultaneously meet the conflicting goals of searching for undiscovered mobile objects whilst keeping track of discovered objects. The planning problem is further complicated by misdetections or false detections of objects caused by range limited sensors and noise inherent to sensor measurements. We formulate a novel multi-objective POMDP based on information theoretic criteria, and an online multi-object tracking filter for the problem. Since controlling multi-agent is a well known combinatorial optimization problem, assigning control actions to agents necessitates a greedy algorithm. We prove that our proposed multi-objective value function is a monotone submodular set function; consequently, the greedy algorithm can achieve a (1-1/e) approximation for maximizing the submodular multi-objective function.