Comparing Distributed Exploration Strategies with Simulated and Real Autonomous Robots

Abstract

This paper presents an experiment in collective robotics in which a group of autonomous robots searches for an infrared target beacon placed in a corner of the exploration area. This task is a more experimentally tractable version of the plume tracing problem, in which robots search for the source of an odor plume. Two different exploration strategies (collaborative and non-collaborative) are implemented and compared on the basis of several team performance metrics. The collaborative strategy uses a simple, binary signaling schema among robots. The experiment is implemented at three different levels: in a physical setup composed of groups of 1 to 8 Moorebot robots, in Webots, a 3D sensor-based, kinematic simulator, and with probabilistic simulations. Results show that the collaborative approach drastically improves the search across several metrics. Furthermore, the probabilistic model qualitatively and quantitatively reproduces the enhanced team performance via collaboration. Additional investigations using the probabilistic model indicate that the optimal number of robots is a function of the ratio between target and exploration areas.