基于速度障碍的控制屏障函数用于动态避障
Dynamic Collision Avoidance Using Velocity Obstacle-Based Control Barrier Functions
AI总结：
本文提出在速度空间构建基于速度障碍的控制屏障函数（VOCBF），结合CLF和决策网络求解MIQP，实现加速度控制独轮机器人在动态环境中的高效避障，并扩展至多机器人系统。
摘要：
为加速度控制的独轮机器人设计安全关键控制器具有挑战性，因为控制输入可能不会出现在控制李雅普诺夫函数（CLFs）和控制屏障函数（CBFs）的约束中，从而导致控制器无效。现有方法通常依赖基于状态反馈的CLF和高阶CBF（HOCBF），这些方法构建计算成本高，并且在存在快速移动、近距离障碍物的动态环境中难以保持有效性。为了解决这些挑战，我们提出在速度空间中构建基于速度障碍的CBF（VOCBF），以增强动态避障能力，而不是依赖需要引入HOCBF的基于距离的CBF。此外，通过使用速度障碍（VO）的变体扩展VOCBF，我们实现了机器人之间的反应式避障。我们将加速度控制的独轮机器人的安全关键控制器构建为混合整数二次规划（MIQP），集成用于导航的基于状态反馈的CLF和用于避障的VOCBF。为了提高MIQP的求解效率，我们将MIQP拆分为多个子优化问题，并采用决策网络来降低计算成本。数值仿真表明，我们的方法能够有效引导机器人到达目标，同时避免碰撞。与HOCBF相比，VOCBF在动态障碍物避让性能上显著提升，尤其是当障碍物快速移动且靠近机器人时。此外，我们将该方法扩展到分布式多机器人系统。
英文摘要：
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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