Driving Swarm: A Swarm Robotics Framework for Intelligent Navigation in a Self-organized World

Abstract

Implementing and conducting reproducible experiments on multi-robot hardware platforms are challenging tasks due to variations in hardware, software, and most importantly the intensive implementation effort. In this paper, we aim to present the Driving Swarm software framework which is developed to facilitate the implementation, deployment, supervision, and analysis of multi-robot experiments. We use this framework with the TurtleBot3 hardware platform and measure its performance for two example scenarios: trajectory tracking and flocking behavior, with 5 and 6 robots. The goal of our experiments is to validate the simulation comparing to hardware implementations and to provide a baseline data for further experiments. While the simulated and real robots show similar behavior, we could observe that the simulated behavior is more robust than the real behavior in both scenarios. This effect is observed in the lower tracking error and better obstacle avoidance in both experiments. While the simulation proved to be a valuable tool during the development of the behaviors, the results confirm the importance of conducting experiments on a real-world test bed.