一个新的路径规划策略,将改进的ACO和DWA算法集成到动态环境中的移动机器人中
Baoye Song1, Shumin Tang1, Yao Li1
1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
Mathematical biosciences and engineering : MBE
|March 8, 2024
概括
本研究介绍了一种新的路径规划策略,用于动态环境中的移动机器人,通过结合改进的群优化 (ACO) 和动态窗口方法 (DWA). 这种综合方法提高了路径效率和避障能力.
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:
- 移动机器人在动态环境中的路径规划面临重大挑战.
- 现有的算法经常在实时避难障碍和效率方面扎.
研究的目的:
- 为移动机器人提出一个新的综合路径规划战略.
- 在动态环境中增强路径最佳性和效率.
主要方法:
- 为静态环境开发了一种改进的殖民地优化 (ACO) 算法,专注于激素初始化,启发函数和更新.
- 为动态环境实施了修改后的动态窗口方法 (DWA),包括节点删除,初始定向优化和改进的评估功能.
- 在各种环境中进行模拟以验证拟议的算法.
主要成果:
- 改进的ACO算法在更少的代中产生了更短的路径,更少的转折点.
- 修改后的DWA有效地避免了移动的障碍物,从而产生了更高效的路径.
- 与现有方法相比,综合方法显示出更高的性能.
结论:
- 拟议的综合路径规划战略有效地解决了在动态环境中移动机器人导航的挑战.
- 改进的ACO和修改的DWA的组合为路径规划提供了强大而高效的解决方案.
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