Distributed Control for Human-Swarm Interaction In Non-Convex Environments using Gaussian Mixture Models

Abstract

This paper presents a novel implementation of a human-swarm interface that allows humans to define an area with desired shape to be reached by a multi-robot system. Human-swarm interaction can be useful in order to exploit human intelligence and knowledge for the operation of swarm robots. The proposed work deals with limitations usually met when dealing with real-world implementation, e.g. limited sensing capabilities of the agents and hard conditions where communication is difficult or even completely denied. Gaussian Mixture Models are exploited in order to define an appropriate probability density function of the environment based on the area selected by a human operator. Then, velocity input for each robot is calculated in a distributed manner using Voronoi tessellation and Lloyd's algorithm. Finally, results of both virtual and real-world tests are presented, showing the final configuration reached by the multi-robot system in comparison with the desired region defined on the graphical interface.