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arXiv 2408.09275cs.RO
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具有自接触特性的模块化刚柔混合机械臂的设计与控制

Design and Control of Modular Soft-Rigid Hybrid Manipulators with Self-Contact

Computer Science and Artificial Intelligence Laboratory, MIT(麻省理工学院计算机科学与人工智能实验室);Delft University of Technology(代尔夫特理工大学);German Aerospace Center (DLR)(德国航空航天中心)

Zach J. Patterson, Emily Sologuren, Cosimo Della Santina, Daniela Rus

AI总结:

针对软机器人负载有限、抗重力作业能耗高的问题,提出串联式刚柔混合机械臂SRH,通过自接触实现刚柔状态切换,设计两种经稳定性证明的控制器并在硬件上验证了多任务执行能力。

摘要:

软机器人技术聚焦于采用高形变材料设计机器人,使其能够在非结构化、多变环境中自适应并安全可靠地作业。尽管软机器人相比刚体机器人具备更高的柔顺性,但其负载能力有限,且在地面环境中对抗重力作业时能耗极高。为解决承载能力受限的问题,我们提出了一类新型刚柔混合机器人机械臂(SRH),采用串联构型集成软连续体模块与刚性关节。SRH机械臂可通过动态形状调制,并利用刚性部件间的自接触有效形成实体结构,从而在柔顺精巧与刚性强力两种状态间无缝切换。我们探讨了SRH机器人的设计与制造方法,并提出了一类适用于SRH系统的新型控制算法,包括构型空间PD+形状控制器和笛卡尔阻抗控制器,二者均经证明具备稳定性,为软机器人提供了必要的底层控制能力。我们在SRH硬件上对控制器进行了验证,展示了机器人完成多项任务的能力。研究结果凸显了刚柔混合范式在打造兼具物理安全性与任务执行效能的机器人方面的潜力。

英文摘要:

Soft robotics focuses on designing robots with highly deformable materials, allowing them to adapt and operate safely and reliably in unstructured and variable environments. While soft robots offer increased compliance over rigid body robots, their payloads are limited, and they consume significant energy when operating against gravity in terrestrial environments. To address the carrying capacity limitation, we introduce a novel class of soft-rigid hybrid robot manipulators (SRH) that incorporates both soft continuum modules and rigid joints in a serial configuration. The SRH manipulators can seamlessly transition between being compliant and delicate to rigid and strong, achieving this through dynamic shape modulation and employing self-contact among rigid components to effectively form solid structures. We discuss the design and fabrication of SRH robots, and present a class of novel control algorithms for SRH systems. We propose a configuration space PD+ shape controller and a Cartesian impedance controller, both of which are provably stable, endowing the soft robot with the necessary low-level capabilities. We validate the controllers on SRH hardware and demonstrate the robot performing several tasks. Our results highlight the potential for the soft-rigid hybrid paradigm to produce robots that are both physically safe and effective at task performance.

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。

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