Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones

TL;DR

This paper introduces a resilient multi-robot coordination framework for target tracking in unknown environments with sensing and communication danger zones, balancing performance and safety through real-time adaptive optimization.

Contribution

It presents a novel nonlinear optimization approach with soft chance constraints for resilient multi-robot tracking in adversarial, unknown environments, enabling real-time adaptive behavior.

Findings

The method improves tracking robustness in dangerous zones.

Resilience is achieved through probabilistic, temporary failure modeling.

Real-world experiments validate the approach's effectiveness.

Abstract

Multi-robot collaboration for target tracking in adversarial environments poses significant challenges, including system failures, dynamic priority shifts, and other unpredictable factors. These challenges become even more pronounced when the environment is unknown. In this paper, we propose a resilient coordination framework for multi-robot, multi-target tracking in environments with unknown sensing and communication danger zones. We consider scenarios where failures caused by these danger zones are probabilistic and temporary, allowing robots to escape from danger zones to minimize the risk of future failures. We formulate this problem as a nonlinear optimization with soft chance constraints, enabling real-time adjustments to robot behaviors based on varying types of dangers and failures. This approach dynamically balances target tracking performance and resilience, adapting to evolving sensing and communication conditions in real-time. To validate the effectiveness of the proposed method, we assess its performance across various tracking scenarios, benchmark it against methods without resilient adaptation and collaboration, and conduct several real-world experiments.