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

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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