Resilience in multi-robot target tracking through reconfiguration

TL;DR

This paper proposes a reconfiguration approach for multi-robot systems to maintain target tracking performance by adaptively modifying communication links based on sensing quality, using optimization strategies to improve resilience.

Contribution

It introduces two novel mixed integer semi-definite programming formulations for dynamic reconfiguration in multi-robot tracking systems, enhancing resilience and performance.

Findings

Team-centric strategy outperforms agent-centric and greedy methods in simulations.

Reconfiguration improves tracking accuracy when sensing quality deteriorates.

Centralized computation enables effective communication graph modifications.

Abstract

We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target's position using a Distributed Kalman Filter (DKF). We use the trace of each robot's sensor measurement noise covariance matrix as a measure of sensing quality. When a robot's sensing quality deteriorates, the system's communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team's target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active communication links. We propose two mixed integer semi-definite programming formulations (an 'agent-centric' strategy and a 'team-centric' strategy) to achieve this goal. We implement both formulations and a greedy strategy in simulation and show that the team-centric strategy outperforms the agent-centric and greedy strategies.