A modular neural network for control of mobile robots

A new modular neural network architecture and its learning algorithm are proposed for a mobile robot controller. The learning algorithm for the proposed new network architecture is based on a feedback error learning procedure, which requires a feedback controller for training processes. It is not so easy, however, to obtain a robot feedback controller, when the robot control task is much more complex. In the present architecture, the complex robot control task is divided into a couple of small simple tasks, each of which is assigned to each of small network modules, respectively. By dividing the complex task, the simple feedback controllers are assigned to the network modules. Therefore, the neural network in each module can be trained by the feedback error learning scheme. The command to the robots is the weighted sum of the outputs of the modules. The weights for each module are obtained from a neural network which is one of the network modules in our proposed architecture. The present neural network architecture and learning algorithm are applied to a set of several robot controllers, whose task is to push a large box to a goal. It is confirmed through computer simulation experiments that the algorithm can train the robot controller skillfully.