冗余机器人手臂的两个加速层配置修改方案基于张神经动力学等价性
Zanyu Tang1,2, Mingzhi Mao3, Yunong Zhang1
1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Biomimetics (Basel, Switzerland)
|July 26, 2024
概括
本研究介绍了机器人手臂的两个新的配置修改 (CA) 方案,利用张神经动力学等价性 (ZNE) 来提高加速层性能并有效地处理物理限制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制理论 控制理论
- 人工智能的人工智能
背景情况:
- 受到限制的冗余机器人手臂在精确的运动控制方面带来了挑战.
- 现有的配置修改 (CA) 方法可能无法完全解决多层物理限制.
研究的目的:
- 为受约束的冗余机器人手臂提出两个新的加速度层CA方案.
- 通过统一约束和考虑时间变化的物理限制来增强机器人手臂控制.
主要方法:
- 张神经动力学等效 (ZNE) 的应用来导出加速度层CA性能指标.
- 理论推导和证明定理和推理,以将角度和速度层的约束转换为加速层.
- 用投影神经动力学解决器解决的标准二次编程问题来制定CA方案.
主要成果:
- 为时间变量物理极限开发一个增强的CA方案,以及为时间不变量极限开发一个简化方案.
- 通过对四环平面臂和UR3空间臂的模拟实验来验证拟议的CA方案.
- 通过Kinova Jaco2手臂的物理实验来证明可行性.
结论:
- 拟议的加速层CA方案有效地解决了被限制的冗余机器人手臂的配置更改.
- 采用ZNE方法为开发先进的CA策略提供了一个强大的框架.
- 开发的方案证明了在现实世界机器人系统中卓越的性能和实际应用性.
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