一种基于Bi-RRT算法的狭窄通道的新型AGV路径规划方法,具有失败率值
Bin Wu1, Wei Zhang1, Xiaonan Chi1
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
新的窄通道路径查找算法 (NCB-RRT) 显著提高了机器人在狭小空间的导航. 它通过优化采样和修剪冗余节点来实现更快的融合和更短的路径.
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
- 路径规划算法 路径规划算法
背景情况:
- 快速探索随机树 (RRT) 算法在狭窄通道环境中难以提高效率,导致收缓慢和高路径成本.
- 现有的算法往往无法有效地适应复杂,狭窄的空间,限制了它们在机器人技术中的实际应用.
研究的目的:
- 引入一种新的路径查找算法,即窄通道路径查找算法 (NCB-RRT),旨在克服在狭窄环境中的RRT限制.
- 提高融合速度,降低路径成本,提高窄通道路径规划的成功率.
主要方法:
- 开发了基于Bi-RRT的NCB-RRT,结合了研究失败率值 (RFRT) 概念.
- 实施了实时采样失败率的三阶段搜索策略和双树搜索的平衡策略.
- 集成父节点重新选择,路径修剪和细分的二次方位贝齐尔曲线用于路径优化.
主要成果:
- 与RRT,RRT*和Bi-RRT相比,NCB-RRT在复杂的窄通道环境中表现出更好的适应性.
- 在路径长度方面取得了显著的改进,减少了冗余节点和拐点,并提高了轨道平滑性.
- 报告的成功率增加了2400% (与RRT相比),1900% (与RRT*相比) 和11.11% (与Bi-RRT相比).
结论:
- 在具有挑战性的狭窄道环境中,NCB-RRT为高效路径规划提供了强大的解决方案.
- 算法的新策略导致了更快的融合,更短的路径和更光滑的轨迹,超过了现有的方法.
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