Control of an unmanned aerial vehicle using a neuronal network

The need for an unmanned aerial vehicle (UAV) controller to operate autonomously and to manage its operations with minimal assistance from humans or rule-based controllers has steadily increased over the years. Numerous approaches have been attempted to address the challenge of developing a UAV with full autonomy. In this paper, a neuronal network-based learning model named NeuraBASE is presented as a possible solution towards autonomy. This neuronal network represents a learning hierarchy of interconnected neurons capable of storing sequences of sensor and motor neuron events. The model is evaluated using experimental scenarios simulated with the STAGE simulation platform, which involves navigational control towards a stationary target. Results show that navigational control with a simple neuronal network can be achieved.