Individual and cooperative tasks performed by autonomous MAV Teams driven by embodied neural network controllers

The work presented here focuses on the use of embodied neural network controllers for MAV (micro-unmanned aerial vehicles) teams. The computer model we have built aims to demonstrate how autonomous controllers for groups of flying robots can be successfully developed through simulations based on multi-agent systems and evolutionary robotics methodologies. We first introduce the field of autonomous flying robots, reviewing the most relevant contributes on this research field and highlighting the elements of novelty contained in our approach. We then describe the simulation model we have elaborated and the results obtained in different experimental scenarios. In all experiments, MAV teams made by four agents have to navigate autonomously through an unknown environment, reach a certain target and finally neutralize it through a self-detonation. The different setups comprise an environment with various obstacles (skyscrapers) and a fixed target, one with a moving target, and one where the target (fixed or moving) needs to be attacked cooperatively in order to be neutralized. The results obtained show how the evolved controllers are able to perform the various tasks with an accuracy level between 72% and 94% when the target has to be approached individually. The performance slightly decreases only when the target is both able to move and can only be neutralized through a coordinated operation. The paper ends with a discussion on the possible applications of autonomous MAV teams to real life scenarios.