提出冲突感知切换策略，提升多目标导航安全与效率
Conflict-Aware Switching for CBF-CLF-Based Multi-Goal Navigation
- 检测CBF与CLF约束冲突，动态切换控制目标以缓解矛盾
- 在多目标场景中减少完成时间与超时率，性能优于基线方法
- 适合多智能体安全导航，兼顾目标达成与实时性
基于控制屏障函数（CBF）与控制李雅普诺夫函数（CLF）的二次规划（QP）广泛用于避障导航的安全控制。然而，CBF与CLF约束固有的冲突会导致性能下降，如减速甚至死锁。这一问题在多目标场景中尤为严重，因多个目标需共享安全约束。现有预判式安全方法通常计算成本高或过于保守，而放松或切换目标的方法又不适用于顺序目标导航。为此，本文提出一种冲突感知切换策略，可检测高冲突状态并动态切换可用的名义控制目标，以降低约束冲突。该方法应用于多智能体、多目标的避障导航场景，相较于基线顺序目标遍历策略，显著减少了完成时间和超时率，在满足所有名义控制目标的同时保证了安全性。
原文摘要 · Abstract (English)
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.
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