将数据融合应用于基于领导者的蝙蝠算法,以改善移动机器人的本地化
Wolmar Araujo-Neto1, Leonardo Rocha Olivi2, Daniel Khede Dourado Villa1
1Department of Electrical Engineering, Universidade Federal do Espírito Santo, Vitória 29075-910, ES, Brazil.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
基于领导者的蝙蝠算法 (LBBA) 通过使用机器人定向来提高移动机器人路径规划,以实现更好的本地化. 这种生物启发的方法提供了更高的准确性和更快的收与更少的粒子,超过其他算法.
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:
- 自主移动机器人需要在复杂的环境中高效地规划路径.
- 生物启发的算法提供了解决方案,但随着粒子数量的增加,它们面临着计算挑战.
- 现有的方法难以处理局部最小值和计算开销.
研究的目的:
- 引入和评估基于领导者的蝙蝠算法 (LBBA) 以提高移动机器人的本地化.
- 为了提高路径准确性和降低自主导航的计算成本.
- 在资源有限的环境中解决传统蝙蝠算法的局限性 (BA).
主要方法:
- 开发了基于领导者的蝙蝠算法 (LBBA),增强了传统的蝙蝠算法 (BA).
- 通过数字指南针获得的机器人定向,被动态集成到小群分布中.
- 实验涉及移动机器人导航充满障碍的环境,比较LBBA与MRFO和BWO.
主要成果:
- 与MRFO和BWO相比,LBBA显示出更高的路径精度和更快的融合.
- 该算法使用更少的粒子实现了这些结果,减少了计算开销.
- 在避免局部最小值方面,LBBA表现出一致的稳定性,这是生物启发方法中常见的问题.
结论:
- LBBA是资源有限的移动机器人实时本地化的一个有希望的解决方案.
- 该算法提高了自主移动机器人的引导精度和效率.
- LBBA的表现表明,它有可能在移动机器人应用中得到更广泛的采用.
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