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改进了用于消毒机器人路径规划的RRT*算法.

Haotian Wang1, Xiaolong Zhou1, Jianyong Li1

  • 1Mechanical Engineering College, Beihua University, Jilin 132021, China.

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概括
此摘要是机器生成的。

改进的人工潜力场引导模糊适应快速探索随机树 (APF-GFARRT*) 算法增强了在复杂环境中消毒机器人的路径规划. 这种方法提高了搜索成功率,并减少了寻找路径的时间和成本.

关键词:
人工潜力场 (APF) 是一个人工潜力场.消毒机器人消毒机器人模糊的控制 模糊的控制路径规划路径规划路径规划快速探索随机树 (RRT*) 的方法

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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) 算法在密集的环境中存在弱方向性和低搜索成功率.
  • 消毒机器人需要在复杂,狭窄的空间中高效地规划路径.

研究的目的:

  • 提出一个改进的APF-GFARRT*算法,用于改进消毒机器人的路径规划.
  • 解决现有的基于RRT的算法在搜索效率和成功率方面的局限性.

主要方法:

  • 该APF-GFARRT*算法集成人工潜力场 (APF) 引导采样与模糊的自适应扩展.
  • 模糊控制用于在密集和受限制的环境中适应性调节步骤大小.
  • 该算法在MATLAB中对RRT,RRT*和APF-RRT*进行了验证.

主要成果:

  • 该APF-GFARRT*算法减少了46.52%的平均初始路径搜索时间.
  • 它实现了更快的融合,将平均代数和最终路径成本降低了10.01%.
  • 改进的算法表现出卓越的适应性,增加了路径的成功和产生更平稳的路径.

结论:

  • APF-GFARRT*算法为在具有挑战性的环境中进行机器人路径规划提供了实用和可行的解决方案.
  • 集成APF和模糊逻辑显著提高了勘探速度和路径质量.