模型预测控制用于受约束的机器人操纵器视觉伺服,通过强化学习调整
Jiashuai Li1, Xiuyan Peng1, Bing Li1
1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Nantong street, Harbin 150001, China.
Mathematical biosciences and engineering : MBE
|June 16, 2023
概括
本研究介绍了一个强化学习调整模型预测控制机器人操纵器视觉伺服. 该方法通过优化控制参数以实现更快,更稳定的机器人响应来增强受约束的基于图像的视觉伺服 (IBVS).
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
- 人工智能的人工智能
背景情况:
- 基于限制的图像视觉伺服 (IBVS) 在机器人操纵器控制中提出了挑战.
- 模型预测控制 (MPC) 为处理控制任务中的系统约束提供了一个框架.
研究的目的:
- 提出一种由强化学习 (RL) 调整的新型模型预测控制 (MPC) 策略,用于机器人操纵者的受约束的基于图像的视觉伺服 (IBVS).
- 在视觉服务任务中提高机器人操纵器响应的速度和稳定性.
主要方法:
- 模型预测控制 (MPC) 用于在系统约束下将IBVS任务构成非线性优化问题.
- 在MPC框架内,使用深度独立的视觉伺服模型作为预测模型.
- 基于深度决定性政策梯度 (DDPG) 的强化学习 (RL) 算法用于训练MPC目标函数权重矩阵.
主要成果:
- 拟议的RL调整的MPC策略为机器人操纵器生成了连续的联合信号.
- 控制器使机器人操纵器能够更快地实现所需状态.
- 模拟实验证明了开发的控制策略的有效性和稳定性.
结论:
- 整合RL与MPC为受约束的IBVS提供了一种有效的方法.
- 拟议的方法在机器人操纵器控制的速度和稳定性方面提供了更好的性能.
- 这一策略对需要精确视觉伺服的先进机器人应用具有前景.
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