Neural-based navigation of a differential-drive mobile robot

This paper presents a new neural network based reactive navigation algorithm for wheeled mobile robots (WMR) in unstructured indoor environments. The mobile robot travels to a pre-defined goal position safely and efficiently without any prior map of the environment. This navigation algorithm is optimized by a user-defined objective function which minimizes the traveled distance to the goal position while avoiding obstacles. The network is trained through off-line learning followed by an on-line learning algorithm with guaranteed convergence. The performance of the proposed algorithm is verified over a variety of real unstructured indoor environments using an autonomous mobile robot platform.