Enhancing Security in Multi-Robot Systems through Co-Observation Planning, Reachability Analysis, and Network Flow

2024-03-20

arXiv.org

AI Summary

Malicious control of multi-robot systems (MRS) poses severe security risks, particularly unauthorized intrusion into restricted zones. Method: This paper proposes a safety reinforcement framework integrating collaborative observation planning, ellipsoidal reachability constraints, and network-flow modeling. It introduces an ellipsoid-based over-approximation of reachable sets and a cross-trajectory collaborative observation mechanism to robustly defend against planned-deviation attacks—without altering original robot paths. Additionally, it employs dynamic subteam redundancy reconfiguration and checkpoint-graph-driven network-flow optimization to enhance observation coverage and timeliness. Contribution/Results: Experiments demonstrate that the method effectively prevents compromised robots from evading collaborative observation while entering prohibited areas. It achieves high security, real-time performance, and deployment compatibility across diverse adversarial scenarios, outperforming existing approaches in both robustness and scalability.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper addresses security challenges in multi-robot systems (MRS) where adversaries may compromise robot control, risking unauthorized access to forbidden areas. We propose a novel multi-robot optimal planning algorithm that integrates mutual observations and introduces reachability constraints for enhanced security. This ensures that, even with adversarial movements, compromised robots cannot breach forbidden regions without missing scheduled co-observations. The reachability constraint uses ellipsoidal over-approximation for efficient intersection checking and gradient computation. To enhance system resilience and tackle feasibility challenges, we also introduce sub-teams. These cohesive units replace individual robot assignments along each route, enabling redundant robots to deviate for co-observations across different trajectories, securing multiple sub-teams without requiring modifications. We formulate the cross-trajectory co-observation plan by solving a network flow coverage problem on the checkpoint graph generated from the original unsecured MRS trajectories, providing the same security guarantees against plan-deviation attacks. We demonstrate the effectiveness and robustness of our proposed algorithm, which significantly strengthens the security of multi-robot systems in the face of adversarial threats.

Problem

• Securing multi-robot systems against adversarial control compromises
• Preventing unauthorized access to forbidden areas via co-observation planning
• Ensuring security guarantees against plan-deviation attacks using network flow

Innovation

• Multi-robot co-observation planning algorithm
• Ellipsoidal reachability constraints for security
• Network flow coverage for cross-trajectory observations