Self-Aware Swarm Navigation in Autonomous Exploration Missions

Abstract

A multitude of autonomous robotic platforms collectively organized as a swarm attracts increasing attention for remote sensing and exploration tasks. A navigation system is essential for the swarm to collectively localize itself as well as external sources. In this article, we propose a self-aware swarm navigation system that is conscious of the causality between its position and the localization uncertainty. This knowledge allows the swarm to move in a way to not only account for external mission objectives but also enhance position information. Position information for classical navigation systems has already been studied with the Fisher information (FI) and Bayesian information (BI) theories. We show how to extend these theories to a self-aware swarm navigation system, particularly emphasizing the collective performance. In this respect, fundamental limits and geometric interpretations of localization with generic observation models are discussed. We further propose a general concept of FI and BI based information seeking swarm control. The weighted position Cramér-Rao bound (CRB) and posterior CRB (PCRB) are employed flexibly as either a control cost function or constraints according to different mission criteria. As a result, the swarm actively adapts its position to enrich position information with different emerging collective behaviors. The proposed concept is illustrated by a case study of a swarm mission for gas exploration on Mars.