Speed control of a mobile robot using neural networks and fuzzy logic

When a certain control function is hard to achieve using a single intelligence technique, collaboration between different ones may succeed in performing such a complicated mission. This paper shows a mobile robot playing a significant role in a clean-room medical factory, where it is not recommended for the human to work. In that environment, neural networks and fuzzy logic were combined to form a suitable solution to perform the dedicated missions. In order to perform the speed control of a mobile robot, multi-layered neural networks designed for environmental recognition, and local navigation feed the fuzzy system with signals of change in direction with the nature of the sub-space of the working environment. To prove this concept, a computer based design and test of the computational intelligence system is performed. This system includes three neural controllers for local navigation, two neural networks for environmental recognition, and a fuzzy system for speed control. The system is fed off-line by a simulated model of a laser range-finder. These major components of the control system perform a global neural navigation and a fuzzy-neural speed control that 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, while avoiding obstacles that are found along the way.