QL-HIT2F:一个Q学习辅助的自适应模糊路径规划算法,具有增强的避难障碍
Nana Zhou1, Fengjun Zhou1, Changming Li2
1School of Computer Science, Shandong Xiehe University, Jinan 250109, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究介绍了一个改进的模糊逻辑路径规划算法,用于机器人,QL-HIT2F,提高适应性和避开障碍. 这种新方法克服了先前方法的局限性,使机器人在复杂环境中实现了更强大的机器人导航.
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
- 控制系统 控制系统
背景情况:
- 模糊逻辑方法被广泛用于机器人路径规划.
- 基于遗传算法的层次间隔类型-2模糊 (GA-HIT2F) 系统提供了新的规划能力.
- 现有的GA-HIT2F方法在适应性和灵活性方面存在局限性,阻碍了复杂场景中的应用,并导致规划失败.
研究的目的:
- 解决GA-HIT2F方法在机器人路径规划中的局限性.
- 提出一个增强的Q-学习辅助的自适应层次间隔类型-2模糊 (QL-HIT2F) 算法.
- 改进机器人避免与特殊障碍物的碰撞,并优化角度调整.
主要方法:
- 开发了一个Q-学习辅助的自适应层次间隔类型-2模糊 (QL-HIT2F) 算法.
- 集成的强化学习用于增强避免碰撞.
- 引入了平均障碍方向 (AOO) 以优化角度调整.
- 将补充机器人参数和模糊成员参数集成到强化学习行动空间中.
- 在培训过程中利用元地图和子培训概念.
主要成果:
- 拟议的QL-HIT2F算法在路径规划中显示出更好的适应性和灵活性.
- 通过强化学习提高了避免与特殊障碍物碰撞的能力.
- 优化了使用平均障碍方向 (AOO) 的机器人角度调整.
- 模拟结果验证了QL-HIT2F方法的可行性和有效性.
结论:
- QL-HIT2F算法代表了机器人路径规划的重大进步.
- 整合Q学习和模糊逻辑可以提高复杂环境中的导航.
- 与以前的GA-HIT2F系统相比,提出的方法提供了一个更强大,更适应的解决方案.
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