Clustering objects with robots that do not compute

Abstract

This paper presents a multi-robot solution to the task of object clustering, where the simplicity of the robots is pushed to the extreme that (i) each robot can only detect the presence of (but not the distance to) an object or another robot in its direct line of sight, and (ii) the robots are unable to store previous inputs and cannot perform arithmetic computations. Controllers for the robots were synthesized through an evolutionary robotics approach driven by physics-based simulations. The results show that the problem can be solved even if the robots cannot distinguish between objects and other robots; however, if they are able to make this distinction, the clustering performance is significantly improved. The controllers have been shown to scale well to large numbers of robots and objects and to be robust to noise. The sensor/controller solution was implemented on the e-puck robotic system. Across 10 systematic experiments with 5 robots and 20 objects, on average, 86.5% of the objects were in one cluster after 10 minutes. We believe that the sensor/controller simplicity paves the way for the implementation of multi-robot systems at very small scales, as required, for instance, in nanomedical applications.