让不同能力的机器人安全协作，避免碰撞或死锁。
Capability-Aware Heterogeneous Control Barrier Functions for Decentralized Multi-Robot Safe Navigation
- 用统一模型处理有无轮子的机器人，保持安全区域不变。
- 根据机器人运动能力分配避障责任，能力强的多承担。
- 实测30个异构机器人仍能安全高效运行，适合真实场景。
多机器人系统在去中心化决策下实现安全导航，需在不牺牲任务效率的前提下保障安全。现有方法常假设机器人同质，导致异构机器人因动力学结构差异对共享安全要求理解不一，部分机器人可能无法物理实现避让动作，引发安全违规或死锁。本文提出能力感知的异构控制屏障函数（CA-HCBF），一种去中心化框架，实现一致的安全约束与能力感知协调。通过典范变换与后推法，将完整运动与非完整运动机器人统一为二阶控制仿射表示，在加速度层控制下保持安全集前向不变性，避免异构动力学间的相对阶失配。进一步引入基于支撑函数的方向能力度量，量化各机器人执行运动意图的能力，推导出按能力比例分配避障责任的配对机制。可行性感知裁剪机制限制每台机器人可实现的约束范围，缓解密集去中心化CBF设置中不可行约束分配的问题。仿真含最多30台异构机器人，以及物理多机器人实验验证了该方法在安全性与任务效率上优于基线，证明其在具有不同运动约束的机器人中具备实际应用价值。
原文摘要 · Abstract (English)
Safe navigation for multi-robot systems requires enforcing safety without sacrificing task efficiency under decentralized decision-making. Existing decentralized methods often assume robot homogeneity, making shared safety requirements non-uniformly interpreted across heterogeneous agents with structurally different dynamics, which could lead to avoidance obligations not physically realizable for some robots and thus cause safety violations or deadlock. In this paper, we propose Capability-Aware Heterogeneous Control Barrier Function (CA-HCBF), a decentralized framework for consistent safety enforcement and capability-aware coordination in heterogeneous robot teams. We derive a canonical second-order control-affine representation that unifies holonomic and nonholonomic robots under acceleration-level control via canonical transformation and backstepping, preserving forward invariance of the safe set while avoiding relative-degree mismatch across heterogeneous dynamics. We further introduce a support-function-based directional capability metric that quantifies each robot's ability to follow its motion intent, deriving a pairwise responsibility allocation that distributes the safety burden proportionally to each robot's motion capability. A feasibility-aware clipping mechanism further constrains the allocation to each agent's physically achievable range, mitigating infeasible constraint assignments common in dense decentralized CBF settings. Simulations with up to 30 heterogeneous robots and a physical multi-robot demonstration show improved safety and task efficiency over baselines, validating real-world applicability across robots with distinct kinematic constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。