运动学
控制理论(社会学)
计算机科学
运动控制
方案(数学)
正确性
透视图(图形)
运动(物理)
机械手
机器人运动学
控制工程
机器人
控制(管理)
人工智能
工程类
数学
移动机器人
算法
物理
经典力学
数学分析
作者
Jialiang Fan,Long Jin,Zhengtai Xie,Shuai Li,Y. Zheng
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2021-11-08
卷期号:18 (8): 5338-5347
被引量:30
标识
DOI:10.1109/tii.2021.3125449
摘要
Redundant manipulators play a critical role in industry and academia, which can be controlled from the kinematic or dynamic perspective. The motion-force control of redundant manipulators is a core problem in robot control, especially for the task requiring keeping contact with objectives, such as cutting, polishing, deburring, etc. However, when a manipulator's model structure is unknown, it is challenging to take motion-force control of redundant manipulators. This article proposes a data-driven-based motion-force control scheme, which solves the motion-force control problem from the kinematic perspective. The scheme can take effect and estimate the structure information, i.e., the model parameters involved in the forward kinematics when the structure of the manipulator is incomplete or unknown. A recurrent neural network is devised to find the solution to the scheme. Besides, the theoretical analysis is presented to prove the correctness of the scheme. Simulations and physical experiments running on seven degrees of freedom redundant manipulators illustrate the superb performance and practicability of the scheme intuitively. The key contribution of this article is that, for the first time, a motion-force control scheme aided with data-driven technology is proposed from a kinematic perspective for the redundant manipulators.
科研通智能强力驱动
Strongly Powered by AbleSci AI