A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

Navigation is one of the most important problems in developing and designing intelligent mobile robots. To locally navigate and autonomously plan a path to arrive to a desired destination, Artificial Neural Networks (ANNs) are employed to model complex relationships between inputs and outputs or to find patterns in data as they provide more suitable solutions than the traditional methods. However, current neural network navigation approaches are limited to one kind of robot platform and range sensor, and usually are not extendable to other types of robots with different range sensors without the need to change the network structures. In this paper, we propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and robot platforms. Moreover, this method contributes positively to reducing the time required for training the networks.