Optimizing Consensus-based Multi-target Tracking with Multiagent Rollout Control Policies

TL;DR

This paper develops a distributed control strategy for multiagent robotic fleets to optimize collective sensing and target tracking by integrating consensus-based information sharing with rollout control within a POMDP framework.

Contribution

It introduces a novel multiagent rollout control policy that maximizes collective sensing information using consensus of information and POMDP modeling.

Findings

Distributed control improves sensing performance.

Consensus of information enhances trajectory prediction.

Simulation results compare centralized and distributed approaches.

Abstract

This paper considers a multiagent, connected, robotic fleet where the primary functionality of the agents is sensing. A distributed multi-sensor control strategy maximizes the value of the collective sensing capability of the fleet, using an information-driven approach. Each agent individually performs sensor processing (Kalman Filtering and Joint Probabilistic Data Association) to identify trajectories (and associated distributions). Using communications with its neighbors the agents enhance the prediction of the trajectories using a Consensus of Information approach that iteratively calculates the Kullback-Leibler average of trajectory distributions, enabling the calculation of the value of the collective information for the fleet. The dynamics of the agents, the evolution of the identified trajectories for each agent, and the dynamics of individual observed objects are captured as a Partially Observable Markov Decision Process (POMDP). Using this POMDP and applying rollout with receding horizon control, an optimized non-myopic control policy that maximizes the collective fleet information value is synthesized. Simulations are performed for a scenario with three heterogeneous UAVs performing coordinated target tracking that illustrate the proposed methodology and compare the centralized approach with a contemporary sequential multiagent distributed decision technique.