Special issue on machine learning for robotics
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EDITORIAL
Special issue on machine learning for robotics Wei Wei1 · Jinsong Wu2 · Chunsheng Zhu3
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
At present, robotics behaves and moves like human, the next step in robotics is towards enhancing robots to think like human and make instantaneous decisions without any human interventions. In order to accomplish this, machine learning algorithms as one important part of artificial intelligence technologies are being introduced into robotics. This special issue on “Machine Learning for Robotics” provides an ideal platform for researchers and technical experts to share their novel works and analysis pertaining to the field of machine learning and its application in robotics. After a strict peer review, 21 papers are selected for publication in this special issue. Details of these selected papers are as follows. The paper entitled “Kinematics Model Identification and Motion Control of Robot Based on Fast Learning Neural Network” provided by Xuehong Sun introduces a new learning neural network structure, called fast learning neural network. Results show that it is appropriate to identify the kinematics model and control the motion of the robot based on fast learning neural network. The paper entitled “Robot Algorithm Based on Neural Network and Intelligent Predictive Control” provided by Yini Wang proposes a novel intelligent predictive control scheme that uses a neural network intelligent predictive controller to control the force/position of the robot. The robustness and rapidity of robots are improved, and good control accuracy and control effects are achieved. The paper entitled “Error Recognition of Robot Kinematics Parameters Based on Genetic Algorithms” provided by Ying Yan constructs the error model of 6-DOF parallel * Wei Wei [email protected] 1
School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2
Department of Electrical Engineering, Universidad de Chile, 833‑0072 Santiago, Chile
3
Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada
manipulator based on genetic algorithms. The simulation results show that the proposed genetic algorithm-based robot kinematics parameter error identification algorithm has obvious advantages. The paper entitled “Path Planning and Control of Soccer Robot Based on Genetic Algorithm” provided by Xuanang Chen, et al. proposes a genetic algorithm for the path planning algorithm of a soccer robot, which enables the robot to find a relatively short path from the point to the target. The paper entitled “Fuzzy Obstacle Avoidance Optimization of Soccer Robot Based on an Improved Genetic Algorithm” provided by Deping Chen studies the method of automatic extraction and optimization of fuzzy rules for fuzzy path planner with the help of an improved genetic algorithm. The paper entitled “Inverse Kinematics Solution of Robotics Based on Neural Network Algorithms” provided by Ruihua Gao proposes a robotics inver
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