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快速探索随机树的最新进展:一篇综述
1School of Information Technology, Jiangsu Open University, Nanjing, 210000, China.
Heliyon
|July 4, 2024
概括
本综述总结了用于机器人路径规划的快速探索随机树 (RRT) 算法的最新进展. 虽然改进的RRT在复杂环境中显示出希望,但超参数调整和硬件可靠性的挑战仍然存在.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 路径规划算法 路径规划算法
- 自主系统 自主系统
背景情况:
- 路径规划是机器人技术的一个关键领域,对于自主导航至关重要.
- 快速探索随机树 (RRT) 算法提供了独特的搜索和采样特性,用于生成高质量的路径.
- 最近的研究 (2021-2023) 专注于在理论上和实际应用中增强RRT算法.
研究的目的:
- 从2021年到2023年提供基于RRT的改进算法的全面审查.
- 突出理论上的进步和RRT的各种应用实施.
- 确定RRT路径规划当前的挑战和未来的研究方向.
主要方法:
- 审查理论改进,包括分支,采样和后处理策略,以及基于模型的RRT.
- 详细检查各种机器人中的RRT应用:接,组装,搜救,手术,太空和检查.
- 分析超参数设计,概括和硬件可靠性的挑战.
主要成果:
- 基于RRT的算法在大规模,实时和不确定的环境中表现出优势.
- 模型驱动的RRT促进了难以量化的策略的设计.
- 在超参数调整,概括和硬件集成方面仍然存在重大挑战,以实现长期稳定.
结论:
- 尽管取得了进展,但RRT路径规划在超参数优化和概括方面面临着障碍.
- 硬件可靠性,机器人本地化,场景建模和多机器人协作是影响RRT性能的关键因素.
- 未来的趋势包括多机器人和人机器人协作,实时规划,自调算法和动态环境适应.
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