Adaptive and Risk-Aware Target Tracking with Heterogeneous Robot Teams

TL;DR

This paper presents a control framework for heterogeneous robot teams to track targets while managing sensor failures, balancing performance and sensor preservation through predictive and reactive strategies.

Contribution

It introduces a novel risk-aware control framework explicitly considering heterogeneous sensors and sensor failure risks for improved target tracking.

Findings

Framework effectively balances performance and sensor preservation.

Simulated experiments validate robustness against sensor failures.

Heterogeneous sensing capabilities enhance tracking efficiency.

Abstract

We consider a scenario where a team of robots with heterogeneous sensors must track a set of hostile targets which induce sensory failures on the robots. In particular, the likelihood of failures depends on the proximity between the targets and the robots. We propose a control framework that implicitly addresses the competing objectives of performance maximization and sensor preservation (which impacts the future performance of the team). Our framework consists of a predictive component -- which accounts for the risk of being detected by the target, and a reactive component -- which maximizes the performance of the team regardless of the failures that have already occurred. Based on a measure of the abundance of sensors in the team, our framework can generate aggressive and risk-averse robot configurations to track the targets. Crucially, the heterogeneous sensing capabilities of the robots are explicitly considered in each step, allowing for a more expressive risk-performance trade-off. Simulated experiments with induced sensor failures demonstrate the efficacy of the proposed approach.