Mobile robots׳ modular navigation controller using spiking neural networks

Autonomous navigation plays an important role in mobile robots. Artificial neural networks (ANNs) have been successfully used in nonlinear systems whose models are difficult to build. However, the third generation neural networks – Spiking neural networks (SNNs) – contain features that are more attractive than those of traditional neural networks (NNs). Because SNNs convey both temporal and spatial information, they are more suitable for mobile robots׳ controller design. In this paper, a modular navigation controller based on promising spiking neural networks for mobile robots is presented. The proposed behavior-based target-approaching navigation controller, in which the reactive architecture is used, is composed of three sub-controllers: the obstacle-avoidance SNN controller, the wall-following SNN controller and the goal-approaching controller. The proposed modular navigation controller does not require accurate mathematical models of the environment, and is suitable to unknown and unstructured environments. Simulation results show that the proposed transition conditions for sub-controllers are feasible. The navigation controller can control the mobile robot to reach a target successfully while avoiding obstacles and following the wall to get rid of the deadlock caused by local minimum.