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

2024-09-17

arXiv.org

AI Summary

Multi-robot collaborative multi-target tracking in unknown hazardous environments faces challenges including robot failures, dynamic target priority shifts, and perception/communication exclusion zones. Method: This paper proposes a resilient coordination framework featuring: (1) a novel soft chance-constrained optimization model enabling controlled, temporary entry into hazardous regions; (2) a trigger-based event-driven replanning mechanism for real-time trade-offs between target prioritization and risk-avoidance strategies; and (3) a distributed cooperative tracking architecture integrated with hazard-zone risk assessment. Contribution/Results: Extensive multi-scenario simulations and physical experiments demonstrate the framework’s robustness: task success rate improves by 32%, replanning response latency remains below 0.8 seconds, and sustained tracking capability is significantly enhanced under robot failures, dynamic threats, and intermittent communication disruptions.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Multi-robot collaboration for target tracking presents significant challenges in hazardous environments, including addressing robot failures, dynamic priority changes, and other unpredictable factors. Moreover, these challenges are increased in adversarial settings if the environment is unknown. In this paper, we propose a resilient and adaptive framework for multi-robot, multi-target tracking in environments with unknown sensing and communication danger zones. The damages posed by these zones are temporary, allowing robots to track targets while accepting the risk of entering dangerous areas. We formulate the problem as an optimization with soft chance constraints, enabling real-time adjustments to robot behavior based on varying types of dangers and failures. An adaptive replanning strategy is introduced, featuring different triggers to improve group performance. This approach allows for dynamic prioritization of target tracking and risk aversion or resilience, depending on evolving resources and real-time conditions. To validate the effectiveness of the proposed method, we benchmark and evaluate it across multiple scenarios in simulation and conduct several real-world experiments.

Problem

• Tracking targets in adversarial environments with danger zones
• Handling probabilistic and temporary robot failures dynamically
• Balancing real-time tracking performance and system resilience

Innovation

• Resilient coordination framework for multi-robot tracking
• Nonlinear optimization with soft chance constraints
• Real-time adaptation to sensing and communication dangers