期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 26, 期 6, 页码 3232-3242出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2021.3056409
关键词
Manipulators; Mathematical model; Robots; Neural networks; Task analysis; Service robots; Planning; Neural network; periodic noise; quadratic programming (QP); redundant robot manipulator
类别
资金
- National Natural Science Foundation [61976096, 61603142, 61633010]
- Guangdong Basic and Applied Basic Research Foundation [2020B1515120047]
- Guangdong Foundation for Distinguished Young Scholars [2017A030306009]
- Guangdong Special Support Program [2017TQ04X475]
- Science and Technology Program of Guangzhou [201707010225]
- Fundamental Research Funds for Central Universities [x2zdD2182410]
- Scientific Research Starting Foundation of South China University of Technology
- National Key Research and Development Program of China [2017YFB1002505]
- National Key Basic Research Program of China (973 Program) [2015CB351703]
- Guangdong Key Research and Development Program [2018B030339001]
- Guangdong Natural Science Foundation Research Team Program [1414060000024]
This article introduces the use of a circadian rhythms neural network (CRNN) to solve motion planning problems of redundant robot manipulators suffering from periodic noise, showing better robustness and performance than traditional neural networks. Comparative simulations and physical experiments further validate the effectiveness and practicality of the proposed CRNN method.
Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness, and practicability of the proposed CRNN.
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