Neurovectors for Drone Flight Planning

Abstract

The usage of a completely bio-inspired neuronal model in the navigation of drones is a challenging process. In this thesis, we propose several neuron models for different drone flight scenarios that allow a drone to navigate its way and execute certain tasks using only biologically plausible approaches. We proposed three scenarios, the first one focused on motion camouflage which is most frequently used when an attacker mimics the optic flow of the background. The second scenario handled the tracking of both ground and flying targets, and in the last one, we proposed a cooperative system for drones to provide cellular wireless covering to ground users