Distributed Multi-Robot Active-Sensing of a Diffusive Source

Abstract

This paper considers the problem of coordinating a group of mobile robots for distributedly estimating the parameters of a diffusion model that generates a time-varying spatial field. We assume that each robot can measure the local concentration of a substance continuously released in the environment and base the proposed distributed estimation strategy on an Extended Information Consensus Filter (E-ICF) with a forgetting factor. We then develop a decentralized online motion strategy aimed at minimizing a Gramian-based information metric that improves the E-ICF convergence. Additional constraints, among which collision avoidance, are integrated as Control Barrier Functions (CBFs) in a Quadratic Program (QP). Finally, we present statistical comparisons against three baselines which show the improved performance of the proposed method in a range of simulated scenarios, and we also report the results of experiments carried out with quadcopters to demonstrate the actual implementability of the approach and its effectiveness in generating online, collision-free, and informative motions.