在不平等约束下,冗余机器人的实时优化反向动力学
Linlin Zhang1, Huibin Du2, Zhiying Qin3
1School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China.
Scientific reports
|November 29, 2024
概括
本研究介绍了一种有效的实时逆动力学方法,用于冗余机器人,克服计算挑战和硬极限违规. 该方法确保机器人可以安全有效地执行复杂的,由传感器驱动的任务.
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
- 计算几何学的计算几何学
背景情况:
- 冗余的机器人由于非唯一的解决方案和计算效率低下而带来逆动力学挑战.
- 现有的方法在实时性能和严格遵守联合和笛卡尔空间限制方面扎.
研究的目的:
- 开发一种通用和高效的方法,用于实时优化冗余机器人的逆动力学.
- 为了在联合和卡尔特斯空间中纳入不可侵犯的严格界限.
- 为了实现传感器驱动的在线任务的实时运动控制.
主要方法:
- 使用受约束的线性编程 (LP) 而不是二次编程 (QP) 进行反向动力学.
- 作为不平等约束,明确处理硬极限 (联合距离,速度,加速度).
- 通过对7-DOF KUKA IIWA机器人进行模拟和实验来验证该方法.
主要成果:
- 受约束的LP方法有效地在实时中解决冗余.
- 保证同时遵守所有运动约束,包括硬限制.
- 在动态,传感器驱动的任务中,证明了对冗余机器人的有效实时控制.
结论:
- 拟议的方法为冗余机器人的实时逆动力学提供了强大的解决方案.
- 它成功地解决了计算效率和硬极限违规问题.
- 能够在复杂的环境中实现需要精确和安全的运动控制的先进机器人应用.
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