一个变电站机器人路径规划算法,基于深度强化学习,增强了殖民地优化
Hongwei Zhang1, Lijun Sun1, Weihong Tan1
1Guangzhou Power Supply Bureau, Guangdong Power Grid Co., LTD., Guangdong, China.
Frontiers in robotics and AI
|February 20, 2026
概括
这项研究介绍了替换站机器人的新型路径规划算法,将深度强化学习与殖民地优化相结合. 改进的方法提高了复杂环境中的效率和安全性.
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:
- 由于复杂的电磁场,密集的设备和安全要求,变电站机器人需要先进的路径规划.
- 现有的方法难以应对变电站环境对检查和维护的独特挑战.
研究的目的:
- 为变电站机器人开发一个改进的路径规划算法.
- 提高变电站检查和维护任务的运营效率和安全性.
主要方法:
- 一个协同框架,将深度强化学习 (DRL) 与群优化 (ACO) 结合起来.
- 用激素引导的勘探策略来减少无效的路径寻找.
- 使用ACO路径经验的样本选机制,以促进Q网络培训.
- 决策权重的动态调整,以便从启发式学习逐渐转向自主学习.
主要成果:
- 与基线DRL算法相比,获得了24%的更高样本效率.
- 平均路径长度减少了18%,并表现出优异的动态避障能力.
- 现场验证显示,在真实变电站中,任务完成率提高了14.8%.
结论:
- 拟议的DRL-ACO算法显著超过了变电站路径规划中的最先进方法.
- 混合方法提高了样本效率,路径最佳性和避开障碍的能力.
- 在现实世界变电站环境中证明了实际有效性和改进了任务完成.
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