将改进的快速探索随机树算法应用于狭窄环境中的类似昆虫的移动机器人
Lina Wang1,2, Xin Yang1, Zeling Chen1
1College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
本研究介绍了移动机器人的改进路径规划算法,提高了效率,减少了复杂环境中的路径长度. 新方法显著减少了规划时间和成本,比现有算法表现出更高的性能.
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:
- 智能移动机器人在复杂,狭窄的环境中面临挑战,包括低效的搜索,多余的节点,非平滑的路径和高运营成本.
- 现有的路径规划算法,如快速探索随机树 (RRT),对现实世界机器人应用程序的效率和路径质量有局限性.
研究的目的:
- 开发基于RRT方法的移动机器人的改进路径规划算法.
- 为了提高搜索效率,减少路径长度和成本,并确保路径流,同时遵守机器人约束.
主要方法:
- 实施了目标偏差采样以消除冗余节点和适应性步骤大小策略以改善RRT的局限性.
- 建模和分析移动机器人,以确保路径遵守角度和碰撞约束.
- 使用立方B-spline曲线进行修剪和平滑生成的路径.
主要成果:
- 与RRT,RRT-connect和RRT*算法相比,改进的RRT算法将路径长度减少了高达26.42%,规划时间减少了高达64.96%.
- 在采样时间,节点数和路径长度方面取得了显著的改进,从而降低了成本和提高了速度.
- 通过模拟和物理测试成功验证,使用类似昆虫的机器人在狭窄的环境中,如电梯入口.
结论:
- 建议改进的RRT算法为复杂和狭窄的环境提供了移动机器人路径规划的显著进步.
- 该算法有效地解决了效率,路径质量和成本降低问题,优于传统的基于RRT的方法.
- 物理验证证实了算法的稳定性和在现实场景中的适用性.
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