Distance-Based Multi-Robot Coordination on Pocket Drones

Bastian Broecker, Karl Tuyls, James Butterworth

Abstract: We present a fully realised system illustrating decentralised coordination on Micro Aerial Vehicles (MAV) or pocket drones, based on distance information. This entails the development of an ultra light hardware solution to determine the distances between the drones and also the development of a model to learn good control policies. The model we present is a combination of a recurrent neural network and a Deep Q-Learning Network (DQN). The recurrent network provides bearing information to the DQN. The DQN itself is responsible for choosing movement actions to avoid collisions and to reach a desired position. Overall we are able provide a complete system which is capable of letting multiple drones navigate in a confined space only based on UWB-distance information and velocity input. We tackle the problem of neural networks and real world sensor noise, by combining the network with a particle filter and show that the combination outperforms the traditional particle filter in terms of converge speed and robustness. A video is available at: https://youtu.be/yj6QqhOzpok.