Online Decentralized Perception-Aware Path Planning for Multi-Robot Systems

Abstract

This paper proposes a decentralized and online optimal perception-aware strategy for multi-robot systems. The aim is to maximize the information collected along the planned trajectory about the relative configurations of the robots and, hence, to minimize the localization uncertainty. This is done by leveraging the so-called Constructability Gramian (CG), which can quantify the information about the future state of a nonlinear system. We show that, thanks to a proper change of coordinates, the CG can be computed in a decentralized way with only minor approximations. This allows for formulating an online and decentralized trajectory generation problem for optimal localization. To show the effectiveness of the approach, we consider as case study the localization of a quadrotor group with noisy distance measurements and sensing constraints. The results show the interest of the proposed approach.