基于视觉的四旋翼机自动飞行的层次优化设计,使用强化学习
IEEE transactions on cybernetics
|May 2, 2025
概括
这项研究增强了四旋翼机的自主飞行,使用强化学习来进行复杂的导航. 先进的控制和决策算法使人能够安全有效地穿越狭窄的空间.
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
- 控制系统 控制系统
背景情况:
- 四旋翼无人机被广泛使用,但需要在复杂的环境中提高自主能力.
- 在狭窄空间的自主飞行对当前的无人机技术提出了重大挑战.
- 基于单眼视觉的导航对于使无人机能够在没有外部传感器的情况下运行至关重要.
研究的目的:
- 开发和验证四旋翼自主飞行智能控制和决策框架.
- 为了提高四旋翼导航在狭窄,复杂的环境中的安全性和效率.
- 整合先进的强化学习技术,用于实时无人机控制和路径规划.
主要方法:
- 采用了层次化的强化学习方法,将问题分为控制和决策层.
- 一个平行策略代算法被设计为四旋翼的非非非非线性系统,使用电机速度作为输入.
- 自主决策被建模为马尔科夫决策过程,结合了课程学习机制和优化的近接政策优化 (PPO).
主要成果:
- 拟议的控制器展示了在线学习能力,改善了基本的控制性能.
- 课程学习机制有效地解决了决策过程中的稀疏奖励挑战.
- 优化的PPO提高了为四旋翼飞机开发自主飞行能力的效率.
结论:
- 开发的智能控制和决策方法显著改善了四旋翼机在复杂环境中的自主飞行.
- 层次化的强化学习框架为狭窄的空间穿越任务提供了强大的解决方案.
- 模拟结果验证了拟议方法在现实世界应用中的有效性和潜力.
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