Behaviour control for a mobile robot by a structured neural network
Minoru Sekiguchi, Shigemi Nagata, Kazuo Asakawa · Advanced Robotics · 1991
We have been researching ways to use neurocomputers that have highly parallel data processing and learning functions for robot control. In this paper, a structured network model for robot control and its learning algorithm are presented. There are three requirements for the robots: (1) The robot must be easy to control but the neural network must be sophisticated enough to handle multiple sensor input. (2) The robot must be able to learn easily. (3) The robot must be able to adjust its own actions. We have developed a new mobile mechanism, created a network model, and increased the network learning speed. Sensor signals from the robot are input to the neural network. The network outputs a certain reaction pattern in response to the sensor input. Then the reaction is refined to an ideal one using training patterns. A robot can change its reaction pattern by changing the training pattern. We created two robots with different action patterns: one chases other robots and the other runs away from other robots. We confirmed that a neurocomputer can effectively control robot actions.