在基于分布式强化学习的多结构环境中设计移动机器人的路径规划策略
Anh-Tu Nguyen1, Duc-Duy Pham1, Van-Nghia Le1
1School of Mechanical and Automotive Engineering, Hanoi University of Industry, No. 298, Cau Dien Street, Bac Tu Liem District, 100000, Vietnam.
MethodsX
|August 18, 2025
概括
本研究介绍了移动机器人的新路径规划策略,使用轻量级学习图像解读与实例适应 (LIDIA) 和量子回归深度Q网络 (QR-DQN) 来在未知的环境中进行更安全的导航.
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
- 计算机视觉 计算机视觉
背景情况:
- 移动机器人需要强大的导航策略来适应未知的环境.
- 深度数据中的图像噪声妨碍了对环境的准确感知.
- 现有的路径规划方法与复杂的多对象场景作斗争.
研究的目的:
- 为移动机器人在未知的环境中开发一种新的,集成的路径规划框架.
- 通过提高深度图像质量和不确定性下决策来增强机器人导航能力.
- 为了在复杂的多物体环境中实现无碰撞的导航.
主要方法:
- 整合轻量级学习图像删除与实例适应 (LIDIA) 进行深度图像删除和校准.
- 量子回归深度Q网络 (QR-DQN) 的应用,这是一个分布式增强学习模型,用于路径规划.
- 整合一个子目标机制来管理复杂的导航任务.
主要成果:
- 拟议的框架有效地使用LIDIA对深度图像进行了否定和校准.
- QR-DQN成功地生成了光滑的短距离路径,在各种场景中表现优于其他方法.
- 综合的方法使得在复杂的环境条件下有效的无碰撞航行.
结论:
- 结合LIDIA和QR-DQN框架在移动机器人路径规划方面取得了重大进展.
- 这一策略在复杂,未绘制的环境中增强了机器人的自主性和安全性.
- 该方法在实现高效和流的路径规划方面表现出卓越的性能.
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