一个自适应的离散RNN算法用于姿势协作运动控制受约束的双臂机器人
Yichen Zhang1, Yu Han1, Binbin Qiu1
1School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China.
Frontiers in neurorobotics
|June 6, 2024
概括
本研究介绍了一种用于机器人的双臂姿势协作运动控制 (DAPCMC) 方案,采用适应性泰勒型反复神经网络 (ATT-DRNN) 进行复杂的3D任务的精确和快速控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 机器人中的重复运动控制已经得到了很好的研究.
- 有限的研究存在于3D环境中受限制的双臂机器人的姿势协作运动控制.
- 复杂的任务需要多臂机器人系统的先进控制策略.
研究的目的:
- 开发一种新的双臂姿势协作运动控制 (DAPCMC) 方案,用于3D空间中的受限制机器人.
- 为了应对平衡计算精度和机器人运动控制中的融合速度的挑战.
- 提出一个有效的算法来解决时间变量方程系统 (TVES) 在机器人的问题.
主要方法:
- 为个人机器人手臂建立了最低排位重复运动控制方案.
- 将新的联合限额转换策略整合到DAPCMC计划中.
- 设计了一种新的自适应性泰勒型离散循环神经网络 (ATT-DRNN) 算法来解决TVES问题.
主要成果:
- ATT-DRNN算法在计算准确性和快速融合速度之间取得了卓越的平衡.
- 理论分析证实了算法在精度和趋同率方面的可靠性.
- 数字模拟和对照案例验证了DAPCMC方案和ATT-DRNN算法的有效性.
结论:
- 拟议的DAPCMC方案有效地使双臂机器人的姿势协作运动控制成为可能.
- ATT-DRNN算法为复杂的机器人控制问题提供了强大而高效的解决方案.
- 这项研究提升了双臂机器人在3D环境中执行复杂任务的能力.
相关概念视频
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