一个自主移动机器人路径规划策略,使用增强的粘液模具算法
Ling Zheng1,2, Chengzhi Hong3, Huashan Song4
1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China.
Frontiers in neurorobotics
|November 2, 2023
概括
一个增强的粘液模具算法 (ESMA) 通过整合自适应技术和人工潜力场来改善移动机器人路径规划. 这种方法在动态环境中实现了更短,无碰撞的路径,优于现有的算法.
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
- 优化算法 优化算法
背景情况:
- 路径规划对于自主移动机器人至关重要,影响效率和能源消耗.
- 主要的粘液模具算法 (SMA) 提供了稳定性,但受到局部优化的影响,缺乏动态避障.
- 像SMA-AGDE和LRSMA这样的现有增强显示出改善,但可以进一步优化.
研究的目的:
- 为改进移动机器人路径规划开发一个增强的粘液模具算法 (ESMA).
- 解决SMA的局限性,特别是局部优化和动态障碍回避.
- 验证ESMA在复杂环境中找到最佳,无碰撞路径方面的有效性.
主要方法:
- 增强的粘液模具算法 (ESMA) 是通过整合全球搜索的自适应技术来开发的.
- 在ESMA中集成了一个人工潜力场,以实现动态的障碍回避.
- 在模拟环境中,ESMA的表现与SMA,SMA-AGDE和LRSMA进行了评估.
主要成果:
- 与SMA,SMA-AGDE和LRSMA相比,ESMA证明了最小路径长度的显著减少 (平均减少分别为3.92%,8.93%,2.73%).
- 该算法在静态和动态环境中成功生成了最短的无碰撞路径.
- 与其他算法相比,ESMA还显示了路径最小值和处理时间的减少.
结论:
- 建议的ESMA算法有效地提高了移动机器人路径规划能力.
- 通过改进全球搜索和动态避难障碍,ESMA克服了传统SMA的局限性.
- 欧证监会为现实世界移动机器人导航挑战提供了强大而高效的解决方案.
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