机器人的全球和本地路径规划结合了ACO和动态窗口算法
1Applied Technology College, Soochow University, Suzhou, 215325, China. luyaping19821030@126.com.
Scientific reports
|March 20, 2025
概括
本研究介绍了一种改进的殖民地算法和动态窗口算法,用于机器人路径规划,提高复杂环境中的效率和避开障碍. 新方法显著减少了路径长度,提高了准确性,使机器人导航更安全,更具适应性.
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
- 计算机科学 计算机科学
背景情况:
- 传统的机器人路径规划方法在具有静态和动态障碍的复杂环境中难以提高效率和适应性.
- 有效的路径规划对于机器人安全和高效地完成任务至关重要.
研究的目的:
- 提出一种新的全球和本地路径规划方法,将改进的殖民地和动态窗口算法结合起来.
- 提高机器人路径规划在复杂环境中的优化能力,融合效率和避障性能.
主要方法:
- 实施了一种改进的殖民地算法,具有状激素初始化,自适应式启发式因素调节和分工策略.
- 集成了改进的动态窗口算法,包括路径方向角度评估和动态速度采样优化.
- 结合全球和地方规划方法,以实现全面的路径优化.
主要成果:
- 与基本的群算法相比,实现了30.18%的平均路径减少和98.46%的精度增加.
- 在网格地图中证明了快速融合 (20*20的第23次代,30*30的第81次代),路径长度减少.
- 与现有算法相比,在四个设计环境中展示了优越的路径流性 (0.94,0.91,0.79,0.65).
- 使用改进的动态窗口算法有效避免动态障碍.
结论:
- 拟议的混合路径规划方法显著提高了效率,稳定性和避障能力.
- 在复杂环境中增强自主导航,为工业自动化,服务和勘探机器人提供适应性解决方案.
- 新的策略为机器人路径规划挑战提供了更有效的方法.
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