粒子群算法是基于人工潜力场的移动机器人的路径规划方法
Li Zheng1, Wenjie Yu2, Guangxu Li2
1School of Automation and Electrical Engineering, Chengdu Technological University, Chengdu 611730, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种改进的粒子群算法 (apfrPSO),用于移动机器人路径规划. 这种新的方法提高了路径效率和全球搜索能力,在复杂的环境中优于传统方法.
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
- 计算智能是一种计算智能.
背景情况:
- 路径规划对于移动机器人的自主性和智能性至关重要.
- 传统的粒子群算法在很长的路径,糟糕的全球搜索和局部优化方面扎.
- 现实环境中的障碍需要提高机器人的适应性,准确性和效率.
研究的目的:
- 为移动机器人路径规划提出一个基于现场的人工潜在粒子群算法 (apfrPSO).
- 在复杂的,障碍繁多的环境中解决传统粒子群优化的局限性.
- 改善路径规划准确性,效率和环境适应性.
主要方法:
- 开发了一个基于人工潜力场的粒子群算法 (apfrPSO).
- 通过调整惯性重量和排名粒子位置,纳入了精细的粒子群优化 (rPSO).
- 整合了人工潜力场方法来引导路径生成.
主要成果:
- 拟议的apfrPSO算法证明了与最先进的算法相比具有竞争力的性能.
- 数字实验表明,路线规划效率和准确性得到改善.
- 该算法显示,它有能力在具有障碍的复杂环境中进行导航.
结论:
- apfrPSO算法为移动机器人路径规划提供了一个有希望的解决方案.
- 人工潜力场的整合和精细的粒子群优化有效地解决了现有的局限性.
- 拟议的方法提高了移动机器人的自主性和智能.
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