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arXiv 2503.02408cs.RO
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基于修正运动学学习的空中机械臂预测运动学协调控制

Predictive Kinematic Coordinate Control for Aerial Manipulators based on Modified Kinematics Learning

College of Computer Science and Technology, Zhejiang University(浙江大学计算机科学与技术学院);Westlake University(西湖大学);Westlake Institute for Optoelectronics(西湖光电研究院)

Zhengzhen Li, Jiahao Shen, Mengyu Ji, Huazi Cao, Shiyu Zhao

AI总结:

本文研究空中机械臂的运动学协调控制问题,提出包含基于学习的修正运动学模型和基于权重分配的MPC方案的预测控制方法,显著提升精度并满足更多任务需求。

摘要:

高精度操作一直是空中机械臂的发展目标。本文研究了空中机械臂的运动学协调控制问题。我们提出了一种预测运动学协调控制方法,该方法包括基于学习的修正运动学模型和基于权重分配的模型预测控制(MPC)方案。与现有方法相比,我们提出的方法具有几个吸引人的特点。首先,运动学模型结合了闭环动力学特性和在线残差学习。与不考虑闭环动力学和残差的方法相比,我们提出的方法精度提高了59.6%。其次,提出了一种考虑权重分配的MPC方案,可以协调四旋翼飞行器和机械臂的运动策略。与不考虑权重分配的方法相比,该方法能满足更多任务的需求。所提出的方法通过复杂轨迹跟踪和运动目标跟踪实验进行了验证,结果验证了该方法的有效性。

英文摘要:

High-precision manipulation has always been a developmental goal for aerial manipulators. This paper investigates the kinematic coordinate control issue in aerial manipulators. We propose a predictive kinematic coordinate control method, which includes a learning-based modified kinematic model and a model predictive control (MPC) scheme based on weight allocation. Compared to existing methods, our proposed approach offers several attractive features. First, the kinematic model incorporates closed-loop dynamics characteristics and online residual learning. Compared to methods that do not consider closed-loop dynamics and residuals, our proposed method has improved accuracy by 59.6$\%$. Second, a MPC scheme that considers weight allocation has been proposed, which can coordinate the motion strategies of quadcopters and manipulators. Compared to methods that do not consider weight allocation, the proposed method can meet the requirements of more tasks. The proposed approach is verified through complex trajectory tracking and moving target tracking experiments. The results validate the effectiveness of the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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