工业机器人的路径规划基于自适应领域合作采样算法
Yongbo Zhuang1, Sha Luo1, Qingdang Li1
1College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong Province, China.
Frontiers in neurorobotics
|December 1, 2025
概括
本研究引入了一个自适应现场共同采样 (AFCS) 算法,以增强工业机器人路径规划. AFCS提高了效率和通用性,在复杂的环境中表现优于传统算法.
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
- 算法设计 算法设计
背景情况:
- 工业机器人路径规划面临效率和通用性的挑战.
- 像快速探索随机树 (RRT) 这样的传统算法在复杂环境中存在局限性.
研究的目的:
- 为改进工业机器人路径规划提出一个自适应场共采样 (AFCS) 算法.
- 为了提高路径规划算法的效率,概括能力和稳定性.
主要方法:
- 开发了一个环境复杂性函数,以利用环境信息.
- 引入了一个最佳的抽样策略,以提高RRT算法的效率和方向性.
- 在RRT中集成了一种改进的人工潜力场 (APF) 算法,用于协作节点的确定.
主要成果:
- 与RRT,RRT*和tRRT相比,AFCS在环境适应性,稳定性和效率方面表现出显著的改善.
- 在不同复杂性环境中的模拟实验验证了算法的性能.
- 在ROKAE工业机器人上的实践验证证了算法的适用性.
结论:
- 拟议的AFCS算法有效地解决了工业机器人的传统路径规划方法的局限性.
- AFCS为复杂的机器人路径规划任务提供了强大而高效的解决方案.
- 该算法显示了对现实世界工业应用的巨大潜力.
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