Conflict-Aware Switching for CBF-CLF-Based Multi-Goal Navigation

2026-06-19

AI Summary

This work addresses the performance degradation in multi-objective navigation caused by inherent conflicts between Control Barrier Function (CBF) and Control Lyapunov Function (CLF) constraints, which often lead to excessive conservatism, system slowdowns, or deadlocks—particularly under shared safety constraints among multiple agents. To mitigate this issue, the paper proposes a conflict-aware goal-switching strategy that, for the first time, integrates real-time conflict quantification with a dynamic switching mechanism for nominal control objectives, specifically tailored for sequential multi-goal navigation. Embedded within a CBF-CLF quadratic programming framework, the approach effectively alleviates constraint conflicts while preserving safety, reducing both conservatism and computational overhead. Experimental results demonstrate significant improvements over baseline methods, with notably reduced task completion times and timeout rates, enabling more efficient achievement of all navigation goals under strict safety guarantees.

Abstract

Quadratic programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) are widely used for safe control in reach-and-avoid navigation. However, the inherently conflicting nature of CBF and CLF constraints can lead to performance degradation, including slowdowns and deadlocks. This issue is exacerbated in multi-goal scenarios, where multiple nominal control objectives must be satisfied under shared safety constraints. Existing approaches for preemptive safety are often computationally expensive or overly conservative, while methods that relax or switch between nominal objectives are not well-suited for sequential goal-to-goal navigation. To address these limitations, we propose a conflict-aware switching strategy that detects high-conflict conditions and switches between available nominal control objectives to reduce constraint conflict. We apply this approach to multi-agent, multi-goal reach-and-avoid scenarios under CBF-CLF-QP control. Compared to a baseline sequential goal traversal strategy, our method reduces both completion time and timeout rates, demonstrating improved performance in satisfying all nominal control objectives while respecting safety constraints.

Problem

• Control Barrier Functions
• Control Lyapunov Functions
• multi-goal navigation
• constraint conflict
• safe control

Innovation

• Conflict-Aware Switching
• Control Barrier Functions
• Control Lyapunov Functions
• Multi-Goal Navigation
• Quadratic Programming