使用被动任务空间控制器叠加的冗余机器人安全与柔顺控制
Safe and Compliant Control of Redundant Robots Using Superimposition of Passive Task-Space Controllers
AI总结：
提出一种基于多个被动任务空间控制器叠加的冗余机械臂柔顺控制框架，无需矩阵求逆和优化即可实现本质稳定、数值条件良好且计算成本低廉的控制，在快速变化参考的高要求动态任务中实现亚厘米级跟踪性能，同时保持交互安全和对奇异的稳健性。
摘要：
与环境交互的动态系统（例如在共享工作空间中）的安全与柔顺控制仍然是一个重大挑战。机器人动态模型的不匹配、数值奇异性以及环境的内在不可预测性都是影响因素。阻抗控制器的在线优化在解决这一挑战方面最近显示出了巨大的前景，然而，其性能不够稳健，无法部署在具有挑战性的环境中。本工作提出了一种基于多个被动任务空间控制器在层次结构中叠加的冗余机械臂柔顺控制方法。我们的被动控制器控制框架本质上是稳定的，数值条件良好（因为不需要矩阵求逆），且计算成本低廉（因为不使用优化）。我们利用并为最近提出的一种被动控制器引入了一种新的刚度曲线，该曲线在发散和收敛阶段之间具有平滑过渡，使其特别适合通过叠加组合多个被动控制器。我们的实验结果表明，所提出的方法在具有快速变化参考的高要求动态任务中实现了亚厘米级的跟踪性能，同时保持交互安全和对奇异的稳健性。所提出的框架在不知道机器人动力学的情况下实现了这些结果，并且由于其被动性而本质上是稳定的。数据进一步显示，机器人可以充分利用冗余来保持主要任务的准确性，同时补偿未知的环境交互，这是当前需要准确接触信息的框架无法做到的。
英文摘要：
Safe and compliant control of dynamic systems in interaction with the environment, e.g., in shared workspaces, continues to represent a major challenge. Mismatches in the dynamic model of the robots, numerical singularities, and the intrinsic environmental unpredictability are all contributing factors. Online optimization of impedance controllers has recently shown great promise in addressing this challenge, however, their performance is not sufficiently robust to be deployed in challenging environments. This work proposes a compliant control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy. Our control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimization is used). We leverage and introduce a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. Our experimental results demonstrate that the proposed method achieves sub-centimeter tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. he proposed framework achieves such results without knowledge of the robot dynamics and thanks to its passivity is intrinsically stable. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information.
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。
