Low-Cost Collaborative Localization for Large-Scale Multi-Robot Systems

Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm's overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots

A.: Bayesian rendezvous for distributed robotic systems

Abstract — In this paper, we state, using thorough mathematical analysis, sufficient conditions to perform a rendezvous maneuver with a group of differential-wheeled robots endowed with an on-board, noisy, local positioning system. In particular, we extend the existing framework of noise-free, graph-based distributed control with a layer of Bayesian reasoning allowing to solve the rendezvous problem more efficiently in presence of uncertainties and in a probabilistically sound way. Finally we perform extensive experiments with a team of four real robots, and simulation with their corresponding simulated counterpart, to confirm the benefits of our Bayesian approach. I