使用深度强化学习的绑定移动机器人的无和无接触路径规划
Ryuki Shimada1, Genya Ishigami1
1Graduate School of Integrated Design Engineering, Faculty of Science and Technology, Keio University, Yokohama, Japan.
Frontiers in robotics and AI
|September 26, 2024
概括
本研究介绍了连接移动机器人的无纠和无接触路径规划 (TCFPP). 这种新的方法使用深度强化学习来避免电缆与障碍物和机器人的纠,确保更安全的长期探索.
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
- 路径规划 路径规划
背景情况:
- 连接的移动机器人提供连续的动力和通信,用于扩展探索.
- 电缆与障碍物纠,机器人构成重大操作风险.
- 现有的路径规划方法可能无法充分解决与电缆相关的危险.
研究的目的:
- 开发一种无结和无接触路径规划 (TCFPP) 方法,用于绑定的移动机器人.
- 为了减轻与电缆纠和纠相关的风险.
- 为了提高使用绑定机器人的自主勘探的安全性和可靠性.
主要方法:
- 在深度强化学习中纳入对同位体意识的路径规划.
- 奖励功能的设计是为了惩罚电缆障碍和电缆机器人接触.
- 通过两个不同的初始电缆配置进行评估:顺序部署和预部署的电缆.
主要成果:
- 拟议的TCFPP方法成功生成了将电缆接触最小化的路径.
- 该方法在考虑的两种电缆部署场景中都显示出有效性.
- 模拟结果显示,与天真方法相比,避免接触的方法优越.
结论:
- 深度强化学习与同位体意识的规划是有效的纠免费的路径规划.
- 开发的方法提高了绑定的移动机器人的操作安全性.
- 这种方法可以在复杂的环境中实现更可靠的长期机器人探索.
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