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智能联合空间路径规划:通过目标驱动和潜在的现场策略提高运动可行性
Yuzhou Li1, Yefeng Yang1, Kang Liu1
1Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
本研究引入了用于机器人操纵器路径规划的新算法,该算法可以改善目标指导和避开障碍. 基于目标的双向人工潜力基于现场的快速探索随机树* (GBAPF-RRT*) 在复杂的场景中提供更快的速度和更短的路径.
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
- 控制系统 控制系统
背景情况:
- 传统的机器人操纵器路径规划往往忽视了完全的操纵器碰撞避免.
- 现有的算法面临着高时间复杂性和局部最小值陷入困境的挑战.
研究的目的:
- 提出一个新的算法,GBAPF-RRT*,增强操纵器路径规划.
- 改进目标指导和全面的避免碰撞能力.
主要方法:
- 利用高斯分布用于启发式指导,以加快RRT*探索.
- 集成了一个修改后的排斥功能,以减轻局部最小的捕获.
- 在联合空间进行模拟和物理实验.
主要成果:
- GBAPF-RRT*算法显示了增强的目标指导和障碍回避.
- 与传统方法相比,实现了更快的搜索速度.
- 在复杂的规划环境中生成更短的路径.
结论:
- 拟议的GBAPF-RRT*算法对于机器人操纵器路径规划是有效和优越的.
- 该方法解决了传统方法的局限性,提供了更好的性能.
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