像蛇一样的机器人工作空间解决方法基于改进的蒙特卡洛方法
ZhiYong Yang1, Wang Tian1, HaoYang Wang1
1Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan, Hubei 430068, China.
Applied bionics and biomechanics
|April 14, 2025
概括
这项研究引入了一种改进的蒙特卡洛方法,用于绘制电力线周围的蛇形机器人工作空间. 新方法增强了点分布和边界提取,以精确避免障碍.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算几何学的计算几何学
- 机械工程 机械工程
背景情况:
- 蛇形机器人越来越多地用于基础设施检查,特别是在高压传输电缆上.
- 传统的工作空间确定方法面临着点云边界提取和准确性的挑战.
- 避免障碍物对于复杂环境中的安全和高效的机器人操作至关重要.
研究的目的:
- 开发一种改进的蒙特卡洛方法,以准确确定在高压传输电缆上运行的蛇形机器人的避障工作空间.
- 解决传统蒙特卡洛方法的局限性,包括不均的点分布和边界提取中的困难.
- 为了提高工作空间体积计算的精度,以改善机器人导航.
主要方法:
- 提出了一种改进的蒙特卡洛方法,从传统蒙特卡洛方法生成的种子工作空间开始.
- 种子工作空间被包裹,分成较小的立方体,然后使用高斯分布概率密度函数扩展.
- 阿尔法形算法被用来提取点云边界,并准确计算工作空间体积.
主要成果:
- 改进的方法有效地解决了点分布不均和边界提取困难的问题.
- 高斯分布采样增强了机器人的工作空间的生成.
- 阿尔法形态算法准确地重建工作空间边界,通过模拟实验验证.
结论:
- 拟议的改进的蒙特卡洛方法,结合alpha-shape算法,为确定蛇形机器人工作空间提供了准确可靠的方法.
- 这种方法显著改进了传统技术,在电力线路检查场景中提供了更高的准确度来避免障碍.
- 精确的工作空间确定对于提高蛇形机器人的安全性和效率在具有挑战性的工业应用中至关重要.
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