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arXiv 2405.02495cs.RO
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用于上肢辅助机器人的模糊逻辑参数调节器设计

Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robots

University of Massachusetts Lowell(马萨诸塞大学洛厄尔分校)

Christopher Coco, Jonathan Spanos, Hamid Osooli, Reza Azadeh

AI总结:

针对上肢辅助外骨骼机器人超参数需频繁手动调节的问题,本文提出基于专家规则的模糊逻辑控制器,根据肌肉激活动态调节增益参数,并在三项任务中验证其能有效减少人机对抗。

摘要:

辅助外骨骼机器人正在帮助患有潜在疾病的人恢复功能。这些机器人需要对超参数进行精确调节,以使用户感觉自然。设备的超参数通常需要在不同任务之间重新调节,这可能十分繁琐且需要专业知识。为了解决这一问题,我们开发了一组模糊逻辑控制器,能够动态调节机器人增益参数,以适应根据肌肉激活确定的用户意图。所设计的模糊控制器受益于一组专家定义的规则,并且不依赖大量训练数据。我们通过三项不同任务对所设计的控制器进行评估,并将结果与手动调节的系统进行比较。初步结果表明,我们的控制器减少了设备与人体之间的对抗程度,该对抗程度通过一组压力传感器进行测量。

英文摘要:

Assistive Exoskeleton Robots are helping restore functions to people suffering from underlying medical conditions. These robots require precise tuning of hyper-parameters to feel natural to the user. The device hyper-parameters often need to be re-tuned from task to task, which can be tedious and require expert knowledge. To address this issue, we develop a set of fuzzy logic controllers that can dynamically tune robot gain parameters to adapt its sensitivity to the user's intention determined from muscle activation. The designed fuzzy controllers benefit from a set of expert-defined rules and do not rely on extensive amounts of training data. We evaluate the designed controllers with three different tasks and compare our results against the manually tuned system. Our preliminary results show that our controllers reduce the amount of fighting between the device and the human, measured using a set of pressure sensors.

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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