如何安排机器人环境? 在运动规划和环境优化方面发挥经验
Jiaxi Lu1, Ryota Takamido2, Yusheng Wang2
1Department of Precision Engineering, School of Engineering, The University of Tokyo, Tokyo, Japan.
Frontiers in robotics and AI
|December 2, 2024
概括
本研究介绍了机器人系统设计的层次算法,它结合了运动规划和环境安排. 它使用经验重用来优化机器人路径,大大减少复杂和简单任务的长度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 机器人系统环境设计是一个高维优化问题.
- 它涉及整合运动规划 (m-DOF) 和环境安排 (免费DOF).
- 由于复杂,高维的搜索空间,现有的方法难以找到最佳的解决方案.
研究的目的:
- 提出基于经验的层次结构优化算法.
- 为了有效地解决运动规划和环境布局的综合问题.
- 改进机器人系统中的路径长度和环境布局.
主要方法:
- 开发了一个分层的等级算法,用于环境排列 (更高) 和运动规划 (更低).
- 实施了经验重复使用的方式,以利用过去的结果来解决新的问题.
- 提出了caseGA-ERTC算法,利用一个案例注入的遗传算法 (GA) 和经验驱动的随机树 (ERT).
主要成果:
- 在难题中实现了12.59%的路径长度减少,在容易问题中减少了5.05%.
- 在相同的时间限制下,证明了环境安排的改进和更短的路径长度.
- 与最先进的方法相比,在三个小型机器人系统上验证了有效性.
结论:
- 层次的caseGA-ERTC算法有效地优化了机器人系统环境设计.
- 经验重复利用显著提高解决方案的质量和效率.
- 拟议的方法为复杂的机器人规划和安排任务提供了卓越的方法.
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