基于改进的RRT,复杂场景中的操纵器的路径规划算法
Xiqing Zhang1,2, Pengyu Wang1,2, Yongrui Guo1,2
1School of Vehicle and Transportation Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究介绍了一种改进的RRT*算法,用于在复杂,充满障碍物的空间中规划机器人手臂路径. 改进的方法提高了效率,并找到接近最佳的路径,克服了常见的规划失败.
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
- 路径规划 路径规划
背景情况:
- 六度自由度的机器人手臂在3D多障碍环境中面临着挑战.
- 现有的路径规划算法往往受到低采样效率和路径搜索失败的影响.
研究的目的:
- 为机器人手臂路径规划提出一个改进的快速扩展随机树 (RRT*) 算法.
- 在复杂的环境中提高采样效率和路径搜索成功率.
主要方法:
- 引入了一个改进的快速扩展随机树 (RRT*) 算法,称为HP-APF-RRT*.
- 整合了一个人工潜力场 (APF) 来引导采样点绕过障碍物.
- 通过生成多个候选点并使用启发式值来优化抽样.
- 使用三角形不等式和冗余节点精细化简化了最终路径.
主要成果:
- 该HP-APF-RRT*算法证明了更好的采样效率和减少不必要的节点.
- 整合APF增强了在障碍密集地区的路径搜索,产生了近乎最佳的路径.
- 与现有算法相比,模拟和物理实验证实了拟议方法的有效性和优越性.
结论:
- 改进的HP-APF-RRT*算法为机器人手臂路径规划在具有挑战性的3D环境中提供了强大的解决方案.
- 该方法有效地解决了采样效率低和路径搜索失败的问题.
- 改进的算法为更高效,更可靠的机器人手臂操作提供了基础.
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