提出新方法让机械臂在动态障碍中安全快速避障。
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
- 引入自适应循环不等式，结合障碍速度与机器人控制方向
- 解决传统方法的伪平衡问题，在动态场景中可靠避障
- 适合需全肢体避障的移动机械臂系统，如人机交互任务
移动操作臂的全身反应式避障仍是开放问题。控制屏障函数（CBF）与二次规划（QP）结合已成为具有安全保证的反应式控制主流方法。但传统CBF方法常出现伪平衡问题（PEP），且难以应对动态障碍。为此，本文提出自适应循环不等式（ACI）方法，综合考虑障碍速度与机器人名义控制，定义方向性安全约束。将其加入CBF-QP框架后，可有效避免PEP，并实现动态环境下的可靠碰撞规避。我们在包含低维移动基座与高维机械臂的移动操作臂上验证了该方法的通用性。此外，集成了一种简单有效的自碰撞规避方法，实现全身无碰撞运行。大量基准对比与实验表明，该方法在未知和动态场景中表现优异，包括人类挥动棍棒、快速投掷物体等复杂任务。
原文摘要 · Abstract (English)
Whole-body reactive obstacle avoidance for mobile manipulators (MM) remains an open research problem. Control Barrier Functions (CBF), combined with Quadratic Programming (QP), have become a popular approach for reactive control with safety guarantees. However, traditional CBF methods often face issues such as pseudo-equilibrium problems (PEP) and are ineffective in handling dynamic obstacles. To overcome these challenges, we introduce the Adaptive Cyclic Inequality (ACI) method. ACI takes into account both the obstacle's velocity and the robot's nominal control to define a directional safety constraint. When added to the CBF-QP, ACI helps avoid PEP and enables reliable collision avoidance in dynamic environments. We validate our approach on a MM that includes a low-dimensional mobile base and a high-dimensional manipulator, demonstrating the generality of the framework. In addition, we integrate a simple yet effective method for avoiding self-collisions, allowing the robot enabling comprehensive whole-body collision-free operation. Extensive benchmark comparisons and experiments demonstrate that our method performs well in unknown and dynamic scenarios, including difficult tasks like avoiding sticks swung by humans and rapidly thrown objects.
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