Neural control system of a mobile robot

Mobile robots could play a significant role in places where it is impossible for the human to work. In such environments, neural networks, instead of traditional methods, are suitable solutions to locally navigate and recognize the environmentpsilas subspaces. In order to learn and perform two important functions ldquoenvironmental recognitionrdquo and ldquolocal navigationrdquo, multi-layered neural networks are trained to process distance measurements received from a laser range finder. This paper will focus on a computer based design and test of this neural system, that includes three neural controllers for local navigation, and two neural networks for environmental recognition, fed off-line by a simulated model of a laser range-finder. These neural networks are the major components of a control system that performs a global neural navigation of a mobile robot, which could be used to perform industrial missions within industrial environments. This control system can guide a mobile robot to track its predefined path to arrive to its final goal through a set of sub-goals, or autonomously plan its path to arrive to the desired final goal, and to avoid obstacles that are found along the way.