Bioinspired Multimodal Multipose Hybrid Fingers for Wide-Range Force, Compliant, and Stable Grasping

欠驱动 执行机构 夹持器 软机器人 计算机科学 机电一体化 联轴节(管道) 理论(学习稳定性) 航程(航空) 机器人 控制理论(社会学) 工程类 控制工程 模拟 人工智能 机械工程 机器学习 航空航天工程 控制(管理)
作者
Jiaqi Zhu,Zhiping Chai,Haochen Yong,Yi Xu,Chuan Fei Guo,Han Ding,Zhigang Wu
出处
期刊:Soft robotics [Mary Ann Liebert, Inc.]
卷期号:10 (1): 30-39 被引量:40
标识
DOI:10.1089/soro.2021.0126
摘要

The increasing demand for grasping diverse objects in unstructured environments poses severe challenges to the existing soft/rigid robotic fingers due to the issues in balancing force, compliance, and stability, and hence has given birth to several hybrid designs. These hybrid designs utilize the advantages of rigid and soft structures and show better performance, but they are still suffering from narrow output force range, limited compliance, and rarely reported stability. Owing to its rigid-soft coupling structure with flexible switched multiple poses, human finger, as an excellent hybrid design, shows wide-range output force, excellent compliance, and stability. Inspired by human finger, we propose a hybrid finger with multiple modes and poses, coupled by a soft actuator (SA) and a rigid actuator (RA) in parallel. The multiple actuation modes formed by a pneumatic-based rigid-soft collaborative strategy can selectively enable the RA's high force and SA's softness, whereas the multiple poses derived from the specially designed underactuated RA skeleton can be flexibly switched with tasks, thus achieving high compliance. Such hybrid fingers also proved to be highly stable under external stimuli or gravity. Furthermore, we modularize and configure these fingers into a series of grippers with excellent grasping performance, for example, wide graspable object range (diverse from 0.1 g potato chips to 27 kg dumbbells for a 420 g two-finger gripper), high compliance (tolerate objects with 94% gripper span size and 4 cm offset), and high stability. Our study highlights the potential of fusing rigid-soft technologies for robot development, and potentially impacts future bionics and high-performance robot development.
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