Geng Zihang, Yang Zhiyuan, Xu Wei, Guo Weichao, Sheng Xinjun
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China.
Biomimetics (Basel). 2024 Oct 16;9(10):629. doi: 10.3390/biomimetics9100629.
Future humanoid robots will be widely deployed in our daily lives. Motion planning and control in an unstructured, confined, and human-centered environment utilizing dexterity and a cooperative ability of dual-arm robots is still an open issue. We propose a globally guided dual-arm reactive motion controller (GGDRC) that combines the strengths of global planning and reactive methods. In this framework, a global planner module with a prospective task horizon provides feasible guidance in a Cartesian space, and a local reactive controller module addresses real-time collision avoidance and coordinated task constraints through the exploitation of dual-arm redundancy. GGDRC extends the start-of-the-art optimization-based reactive method for motion-restricted dynamic scenarios requiring dual-arm cooperation. We design a pick-handover-place task to compare the performances of these two methods. Results demonstrate that GGDRC exhibits accurate collision avoidance precision (5 mm) and a high success rate (84.5%).
未来，人形机器人将在我们的日常生活中得到广泛应用。利用双臂机器人的灵活性和协作能力，在非结构化、受限且以人类为中心的环境中进行运动规划和控制仍是一个悬而未决的问题。我们提出了一种全局引导双臂反应式运动控制器（GGDRC），它结合了全局规划和反应式方法的优势。在此框架中，具有前瞻性任务视野的全局规划器模块在笛卡尔空间中提供可行的引导，而局部反应式控制器模块通过利用双臂冗余来解决实时碰撞避免和协调任务约束问题。GGDRC扩展了用于需要双臂协作的运动受限动态场景的基于优化的最新反应式方法。我们设计了一个拾取-交接-放置任务来比较这两种方法的性能。结果表明，GGDRC展现出精确的碰撞避免精度（5毫米）和较高的成功率（84.5%）。
