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一个通过时间变化的惩罚策略合成的双标准避难障碍方案 移动并行操纵器神经网络
IEEE transactions on cybernetics
|November 12, 2025
概括
一个新的双标准避障方案 (BCOA-TVPS) 使用时间变化的惩罚策略神经网络,使移动并行操纵器能够避免障碍物和重复运动,同时最大限度地减少速度峰值.
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:
- 移动并行操纵器需要复杂的控制,以确保安全和高效的操作.
- 避免障碍物和精确的轨迹跟踪是机器人技术中的关键挑战.
研究的目的:
- 为移动并行操纵器提出和设计一个双标准避障方案 (BCOA-TVPS).
- 为了应对在避开障碍时重复运动和速度峰值的挑战.
主要方法:
- 制定了障碍回避作为一个受约束的时间变化的二次编程 (QP) 问题.
- 开发了一种双标准方法,将重复运动和速度最小化结合起来.
- 利用一个时间变量惩罚策略 (TVPS) 神经网络来解决QP问题.
主要成果:
- BCOA-TVPS 方案有效地将避开障碍与重复运动和速度最小化相结合.
- 轨迹跟踪实验证明了该方案的有效性和适用性.
- TVPS神经网络成功解决了复杂的QP问题.
结论:
- 拟议的BCOA-TVPS方案为移动并行操纵器控制提供了一个强大的解决方案.
- 这种方法通过管理障碍物,运动重复和速度峰值来提高安全性和性能.
- 该研究验证了神经网络用于解决机器人学中复杂的优化问题的使用.
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