A real-time, unsupervised neural network for the low-level control of a mobile robot in a nonstationary environment

Abstract

This article introduces a real-time, unsupervised neural network that learns to control a two-degree-of-freedom mobile robot in a nonstationary environment. The neural controller, which is termed neural NETwork MObile Robot Controller (NETMORC), combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. As a result, the controller learns the wheel velocities required to reach a target at an arbitrary distance and angle. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. Aside from being able to reach stationary or moving targets, the NETMORC structure also enables the robot to perform successfully in spite of disturbances in the environment, such as wheel slippage, or changes in the robot's plant, including changes in wheel radius, changes in interwheel distance, or changes in the internal time step of the system. Finally, the controller is extended to include a module that learns an internal odometric transformation, allowing the robot to reach targets when visual input is sporadic or unreliable.