在基于强化学习的高速运动中对可变刚度软的坚固抓取
Mingzhu Zhu1, Junyue Dai2, Yu Feng2
1School of Astronautics, Northwestern Polytechnical University, Xi'an, China.
Soft robotics
|July 21, 2023
概括
这项研究引入了一个深度强化学习控制器来管理软机器人抓手的刚性,有效地减少在高速拾取和放置任务期间的振动,而不会影响软度. 这项创新增强了机器人对微妙物体的把握.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 工业机器人利用选择和放置任务以提高效率,但高速会诱导软机器人抓手中的振动.
- 软抓手中的振动可以损坏微妙的物体,导致抓不住,尽管它们对脆弱的货物有好处.
- 可变刚度软抓旨在抑制振动,但平衡刚度和合规性仍然是一个挑战.
研究的目的:
- 提出一种基于深度强化学习的新型控制器,用于管理软机器人抓柄硬度.
- 在高速操作期间,以精确地抑制软机器人抓手中的振动.
- 为了保持抓手的固有合规性和柔软性,同时减轻振荡.
主要方法:
- 开发一个深度强化学习 (DRL) 控制器,用于实时硬度调整.
- DRL控制器根据当前操作环境估计了最佳硬度输出.
- 使用UR5机器人臂进行实验验证,以证明控制器的有效性.
主要成果:
- 拟议的DRL控制器在抑制振动方面表现出快速响应时间.
- 在各种实验条件下观察到振荡幅度的大幅减少.
- 控制器有效地抑制了振动,对抓柄的遵守和柔软性产生了最小的影响.
结论:
- 深度强化学习为软机器人抓手的实时振动控制提供了一种有效的策略.
- 开发的控制器成功地平衡了对振动缓解的刚度控制与抓手软度的需求.
- 这种方法显著提高了涉及微妙物体的高速取置操作的可靠性.
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