Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging High-Order Control Barrier Functions (HOCBFs) constructed for safe interactions among 3D convex shapes to address this issue. While prior works focused on using Control Barrier Functions (CBFs) for human-robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with many nominal controllers and ensure safety without significantly restricting the robots' workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka Research 3 robotic arms. The project video is available at this link.

翻译：当多个机械臂在近距离协同作业时，机械臂间的碰撞构成了显著的安全风险。为解决这一问题，本文提出了一种在线碰撞规避方法，该方法利用为三维凸形状间的安全交互构建的高阶控制屏障函数（HOCBFs）。先前的研究主要集中于使用控制屏障函数（CBFs）进行人-机械臂以及单机械臂的碰撞规避，而本文则探讨了在共享工作空间中运行的多个机械臂之间的碰撞规避问题。在我们的方法中，我们将所提出的HOCBFs用作集中式和分散式安全滤波器。这些安全滤波器与多种标称控制器兼容，能在不显著限制机器人工作空间的前提下确保安全性。实现这些滤波器的一个关键挑战在于，大量安全约束以及每个约束所需的Hessian矩阵计算所带来的计算开销。我们通过采用数值微分方法来近似计算密集项，以应对这一挑战。我们通过广泛的仿真研究以及使用Franka Research 3机械臂进行的真实世界实验，验证了该方法的有效性。项目视频可通过此链接获取。