基于强化学习的智能路径规划,以在动态环境中实现最佳导航
Anil Kumar Yadav1, Purushottam Sharma2, Xiaochun Cheng3
1VIT Bhopal University, Bhopal-Indore Highway, Bhopal, India.
Neural processing letters
|January 23, 2026
概括
在强化学习 (RL) 中优化奖励功能显著改善了自主移动机器人的导航. 这种增强的RL方法减少了动态环境中的路径距离和学习时间.
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
- 机器学习 机器学习
背景情况:
- 路径选择和规划对于自主移动机器人 (AMR) 进行高效导航和避免障碍物至关重要.
- 传统方法通常使用分析搜索最短路径,但强化学习 (RL) 通过动作序列优化提供了增强的性能.
- 一个常见的RL算法Q-learning由于依赖累积奖励而在动态系统中与环境概括作斗争.
研究的目的:
- 在基于RL的AMR路径规划中,优化奖励功能以实现高效的导航和避开障碍.
- 在动态环境中增强RL算法的概括能力.
- 评估优化奖励机制对路径规划效率和学习绩效的影响.
主要方法:
- 该研究提出了基于RL的路径规划的优化奖励函数,考虑动态环境中的总步骤,计数步骤和折扣率.
- 通过使用优化奖励机制在不同环境中实现和分析状态奖励值.
- 评估了对Q-Learning和深度Q-Learning算法的影响,比较了基于状态-动作对的性能.
主要成果:
- 优化的奖励函数显著减少了学习所需的代和情节的数量.
- 与传统方法相比,总体轨迹距离减少了30%至70%.
- 证明改善了路径优化,学习速率,情节完成和决策准确性.
结论:
- 优化的奖励功能提高了RL在动态环境中的AMR路径规划的有效性.
- 拟议的方法在导航效率和避开障碍方面取得了显著的改进.
- 结合多个代理和先进的技术,如联合和转移学习,可以进一步提高在更大的地图上的融合和性能.
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