使用RRT-APF规划和FNN任务分配的群体控制在移动差分平台上进行了测试
Michal Lajčiak1, Ján Vachálek2
1Department of Mechatronics, Secondary Technical School, 01851 Dubnica nad Vahom, Slovakia.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一个新的集中式机器人群控制系统,集成路径规划和任务分配,以实现高效的实时管理. 这种新的方法在可重新配置的制造系统中提高了群体性能.
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
- 制造系统制造系统的制造
背景情况:
- 机器人群的有效管理对于复杂的任务至关重要.
- 现有的方法经常在实时任务分配和避免碰撞方面扎.
研究的目的:
- 开发一种用于机器人群的新型集中控制方法.
- 整合路径规划和任务分配,以提高群体性能.
- 在可重新配置的制造环境中证明系统的有效性.
主要方法:
- 用feedforward神经网络进行实时任务适应性预测.
- 一个混合算法结合快速探索随机树 (RRT) 和人工潜力场 (APF) 实现了路径规划.
- 纳入多代理路径识别 (MAPF) 来解决同时发生的碰撞.
- 使用了带有差分驱动的实验硬件,使用了ArUco跟踪剂.
主要成果:
- 提出的方法成功地整合了任务分配和路径规划.
- 该系统展示了有效的实时控制和避免碰撞.
- 实验验证证证实了对照溶液的有效性.
结论:
- 这种新型的集中控制方法为机器人群管理提供了强大的解决方案.
- 综合方法在制造应用中显著提高了群体性能.
- 这项工作为先进的群体可重新配置制造系统奠定了基础.
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