在障碍物密集的环境中,使用课程强化学习来规划虫灵感跳跃机器人的路径
Qijie Zhou1,2,3, Gangyang Li1,2, Zhiqiang Yu4
1Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, People's Republic of China.
Bioinspiration & biomimetics
|October 31, 2025
概括
这项研究引入了一个两阶段的课程强化学习 (TS-CRL) 框架,用于虫灵感的跳跃机器人. 在复杂的环境中,TS-CRL可实现高效,无碰撞的路径规划,提高成功率和降低路径成本.
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
- 群集情报 群集情报 群集情报
背景情况:
- 生物灵感的跳跃机器人对复杂的环境显示出希望.
- 在密集的障碍物中大规模跳跃机器人的路径规划是具有挑战性的.
研究的目的:
- 开发一个有效的路径规划框架,以虫为灵感的跳跃机器人.
- 为了使机器人能够在小组任务中进行自主决策.
主要方法:
- 提出了一个两阶段的课程强化学习 (TS-CRL) 框架.
- 使用了一个具有注意力机制的群体不变编码器.
- 采用基于科尔莫戈罗夫-阿诺德网络的演员-关键网络架构.
主要成果:
- TS-CRL为多个机器人生成了高效,无碰撞的路径.
- 与典型的强化学习算法相比,平均路径成本降低了13.7%.
- 机器人到达目标区域的成功率明显提高.
结论:
- 对于以虫为灵感的跳跃机器人来说,TS-CRL是有效和可扩展的.
- 该框架可以在具有挑战性的环境中实现自主导航.
- 通过模拟和现实世界多机器人实验验证.
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