A Predictive Cooperative Collision Avoidance for Multi-Robot Systems Using Control Barrier Function

2025-01-14

AI Summary

Existing control barrier function (CBF) methods for multi-robot coordination ensure only instantaneous safety, often leading to conservatism, control oscillations, and deadlock. To address these issues, this paper proposes a predictive safety control framework. Its key contributions are: (1) a predictive safety matrix incorporating the minimum eigenvalue criterion to verify safety over a finite prediction horizon; (2) a deadlock-escape strategy based on minimal orientation angles, jointly optimizing safety and efficiency; and (3) an integrated architecture combining prediction-horizon optimization, quadratic programming, and kinematic decoupling control. Experiments demonstrate that the method significantly improves robustness against measurement noise, eliminates control oscillations, avoids excessive detours and stagnation, enables rapid deadlock recovery, and yields smooth, collision-free trajectories—thereby enhancing both system stability and operational efficiency.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

Control barrier function (CBF)-based methods provide the minimum modification necessary to formally guarantee safety in the context of quadratic programming, and strict safety guarantee for safety critical systems. However, most CBF-related derivatives myopically focus on present safety at each time step, a reasoning over a look-ahead horizon is exactly missing. In this paper, a predictive safety matrix is constructed. We then consolidate the safety condition based on the smallest eigenvalue of the proposed safety matrix. A predefined deconfliction strategy of motion paths is embedded into the trajectory tracking module to manage deadlock conflicts, which computes the deadlock escape velocity with the minimum attitude angle. Comparison results show that the introduction of the predictive term is robust for measurement uncertainty and is immune to oscillations. The proposed deadlock avoidance method avoids a large detour, without obvious stagnation.

Problem

• Multi-Robot Collaboration
• Collision Avoidance
• Predictive Safety

Innovation

• Predictive Safety Matrix
• Collision Prediction and Avoidance
• Robust Uncertainty Handling