用于避障的移动操作机器人安全快速全身控制
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
AI总结：
针对移动操作机器人全身避障中的伪平衡与动态障碍难题，提出将自适应循环不等式融入CBF-QP，并结合自碰撞避免，实现安全可靠的全身无碰撞控制。
摘要：
移动操作机器人（MM）的全身反应式避障仍是一个开放性研究问题。控制障碍函数（CBF）与二次规划（QP）相结合，已成为实现具有安全保证的反应式控制的流行方法。然而，传统CBF方法往往面临伪平衡问题（PEP）等问题，并且在处理动态障碍物时效果不佳。为克服这些挑战，我们提出了自适应循环不等式（ACI）方法。ACI同时考虑障碍物速度和机器人的标称控制，以定义方向性安全约束。当将其加入CBF-QP时，ACI有助于避免PEP，并能够在动态环境中可靠地避障。我们在一个包含低维移动底盘和高维机械臂的MM上验证了该方法，证明了该框架的通用性。此外，我们集成了一种简单而有效的自碰撞避免方法，使机器人能够实现全面的全身无碰撞运行。大量基准比较和实验表明，我们的方法在未知和动态场景中表现良好，包括避开人挥动的棍棒和快速投掷的物体等困难任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
