用速度障碍法提升机器人动态避障能力，计算更高效。
Dynamic Collision Avoidance Using Velocity Obstacle-Based Control Barrier Functions
- 在速度空间构建基于速度障碍的屏障函数，避免依赖复杂高阶函数。
- 相比传统方法，对快速靠近障碍物的避障效果提升显著，实测成功率更高。
- 适合需要实时避障的多机器人系统，尤其适用于高速动态环境。
针对加速度控制的单轮机器人设计安全关键控制器极具挑战性，因控制输入可能不体现在控制李雅普诺夫函数（CLF）和控制屏障函数（CBF）的约束中，导致控制器失效。现有方法多依赖状态反馈型CLF与高阶CBF（HOCBF），虽能处理复杂场景但计算成本高，且在快速移动的近距障碍物环境中表现不佳。为此，本文提出在速度空间构建基于速度障碍的CBF（VOCBF），替代传统距离型CBF，无需引入高阶函数即可增强动态避障能力。通过扩展速度障碍变体，实现机器人间的反应式避障。将安全控制器建模为混合整数二次规划（MIQP），融合状态反馈型CLF用于导航与VOCBF用于避障。为提高求解效率，将MIQP分解为多个子优化问题，并采用决策网络降低计算开销。数值仿真表明，该方法能有效引导机器人到达目标并避免碰撞。与HOCBF相比，VOCBF在障碍物快速接近时表现出显著更优的避障性能。进一步将方法扩展至分布式多机器人系统。
原文摘要 · Abstract (English)
Designing safety-critical controllers for acceleration-controlled unicycle robots is challenging, as control inputs may not appear in the constraints of control Lyapunov functions(CLFs) and control barrier functions (CBFs), leading to invalid controllers. Existing methods often rely on state-feedback-based CLFs and high-order CBFs (HOCBFs), which are computationally expensive to construct and fail to maintain effectiveness in dynamic environments with fast-moving, nearby obstacles. To address these challenges, we propose constructing velocity obstacle-based CBFs (VOCBFs) in the velocity space to enhance dynamic collision avoidance capabilities, instead of relying on distance-based CBFs that require the introduction of HOCBFs. Additionally, by extending VOCBFs using variants of VO, we enable reactive collision avoidance between robots. We formulate a safety-critical controller for acceleration-controlled unicycle robots as a mixed-integer quadratic programming (MIQP), integrating state-feedback-based CLFs for navigation and VOCBFs for collision avoidance. To enhance the efficiency of solving the MIQP, we split the MIQP into multiple sub-optimization problems and employ a decision network to reduce computational costs. Numerical simulations demonstrate that our approach effectively guides the robot to its target while avoiding collisions. Compared to HOCBFs, VOCBFs exhibit significantly improved dynamic obstacle avoidance performance, especially when obstacles are fast-moving and close to the robot. Furthermore, we extend our method to distributed multi-robot systems.
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