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自主移动机器人以深度强化学习学习的道路

Yu Cao1, Kan Ni1, Takahiro Kawaguchi1

  • 1Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
概括

这项研究增强了自主移动机器人的路径,通过将纯粹的追逐方向盘与深度强化学习相结合,用于自适应的速度控制. 这种新的方法改善了非全方位约束的机器人的路径收和速度调整.

科学领域:

  • 机器人技术和自主系统
  • 人工智能的人工智能
  • 控制理论 控制理论

背景情况:

  • 自主移动机器人在各种应用中至关重要,路径跟踪是其核心能力.
  • 由于速度控制有限或依赖特定路径的速度,当前的路径追踪方法往往缺乏普遍性.

研究的目的:

  • 为非全方位移动机器人开发一种更普遍,更强大的路径跟踪方法.
  • 将传统算法与深度强化学习相结合,以提高自主导航.

主要方法:

  • 这是一种新的方法,它结合了用于转向控制的纯追求算法和用于速度控制的软演员关键深度强化学习算法.
  • 在具有随机生成路径的环境中训练速度控制策略.
  • 通过模拟和实验测试进行验证.

主要成果:

  • 与现有方法相比,在路径收方面取得了显著的改进.
  • 实现了适应性速度调整,以适应不同曲率的路径.
  • 在一个非全方位移动机器人上验证了该方法的有效性.

结论:

  • 集成的纯追求和深度强化学习方法增强了非全方位机器人的路径遵循强度.
关键词:
自主移动机器人 自主移动机器人深度强化学习的学习.沿着路径遵循的路径.柔软的演员 - 批评家控制速度的速度控制器

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  • 这种方法提供了改进的路径收和自适应速度控制,增加了通用性.
  • 该方法显示了对其他非全方位系统的更广泛应用的潜力.