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Variable‐target‐time travel control algorithm of a robot vehicle by a neural network considering the centrifugal force

Minoru KodairaFaculty of Engineering, Yamagata University, Yonezawa, Japan 992Teruhiko OhtomoTeruhiko Ohtomo: graduated from the Yamagata University, Faculty of Engineering, Department of Electrical Engineering, in 1969, and finished the Master's course at the graduate school of the same university in 1971. In the same year he became a research associate at the same university where he became a professor in 1992. He received his Dr. of Engineering degree from Osaka University in 1983. His research interests include speech recognition, recognition of handwritten Chinese characters, and information processing by neural networks. He is a member of the Information Processing Wety of Japan, the Japanese Acoustics Society, the Japanese Neural Network Society, and the society of Instrument and Control EngineersTakao OhuchiTakao Ohuchi: graduated from the Tohoku University, Faculty of Engineering, Department of Electrical Engineering, in 1953, and received the Master's degree from the same university in 1956. In the same year he became a research associate at the department of Electrical Engineering of the Yamagata University. He became a professor at Yamagata University in 1975 and in 1994 transferred to the Nihon University as a professor at the Department of Information Science, Faculty of Engineering. He received the Dr. of Engineering degree from Tohoku University in 1975. He has done research in applications of nonlinear magnetism, systems engineering, pattern recognition, and autonomous moving robots. He is a member of the Society of Electrical Engineers, the Society of Instrument and Control Engineers, the Institute of Systems, Control and Information Engineers, and the Japanese Robot society

Systems and Computers in Japanjournal1996en

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ABI

Annotatsiya

Abstract The derivation of mathematical models for robot vehicles is extremely difficult due to the high non‐linearity of the motion characteristics, making it hard to apply the conventional control theory without using approximation methods which assume constant speed, etc. In this paper, the motion control method using a neural model proposed by Uno, Kawato et al. is extended in such a way to allow its application to the travel control problem of a robot vehicle, which is highly nonlinear with respect to the driving force and steering angle, and a method that generates motion commands and performs trajectory planning at the same time is proposed. In this case, the internal coordinates of the identification module which learns the motion characteristics of the robot vehicle are viewed as motion coordinates, and a method to convert the absolute coordinates for each discrete time into motion coordinates is proposed, making it possible to reduce greatly the number of learning patterns. Furthermore, a variable target time algorithm is proposed that counteracts the centrifugal force effects which act on the robot vehicle when the curvature of the generated trajectory is large. The proposed algorithm restrains the centrifugal force and generates optimal motion commands and trajectory, producing results close to those obtained by a human driver. The effectiveness of the proposed method is demonstrated by computer simulation.

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Mavzular

Vehicle Dynamics and Control Systems Robotic Path Planning Algorithms Autonomous Vehicle Technology and Safety

Identifikatorlar

DOI: 10.1002/scj.4690270908

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