基于预测强化学习的生物仿真自主系统的目标追踪控制
Yu Wang1, Jian Wang2,3, Song Kang2,3
1Department of Automation, Tsinghua University, Beijing 100084, China.
Biomimetics (Basel, Switzerland)
|January 22, 2024
概括
这项研究引入了机器人鱼的仿生控制框架,灵感来自鱼类学校教育. 该系统使用预测强化学习来实现有效的目标追踪和水下机器人的合作控制.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 生物模拟学是一种生物模拟学.
背景情况:
- 鱼群培养行为提高了水力动力学效率.
- 生物仿真方法对于先进的自主系统至关重要.
- 合作控制对于复杂的水下任务至关重要.
研究的目的:
- 为仿生自主系统开发一个针对目标的控制框架.
- 模仿鱼类的训练,以提高机器人鱼的性能.
- 为了实现机器人鱼的合作跟随行为.
主要方法:
- 建立了基于鱼类学校原则的以下运动模型.
- 开发了一个可预测的深度决定性政策梯度 (DDPG) 遵循控制器.
- 集成了一个非线性模型预测控制器与预测强化学习.
主要成果:
- 对单个机器人鱼 (固定和动态) 进行有效的目标追踪.
- 验证了领导者和追随者机器人鱼之间的合作追随能力.
- 预测控制方法在避免超标方面表现出有效性.
结论:
- 拟议的生物模拟控制框架对机器人鱼有效.
- 这项研究为水下机器人的合作控制提供了宝贵的见解.
- 这些方法为使用自主系统加强海洋探索铺平了道路.
相关概念视频
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At the heart...
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Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...


