Logo image
Back
Resilient Multi-Robot Target Tracking with Sensing and Communication Danger Zones
Conference proceeding

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

Peihan Li, Yuwei Wu, Jiazhen Liu, Gaurav S. Sukhatme, Vijay Kumar and Lifeng Zhou
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3573-3580
19 Oct 2025
DOI: https://doi.org/10.1109/IROS60139.2025.11246036

Abstract

Collaboration Probabilistic logic Real-time systems Resilience Robot kinematics Robot sensing systems Sensors Target tracking Optimization Safety
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.

Metrics

25 Record Views

Details

Logo image