Autonomous Navigation of a Robotic Swarm in Space Exploration Missions

In recent years, the paradigm of navigation has shifted from pinpointing the location of a single agent to continuously estimating the full kinematic state of networked autonomous agents. In this paper, we propose a kinematics-aware information seeking algorithm for swarm navigation. The algorithm tightly couples state estimation and autonomous control given ranging and kinematic models. With the help of the Fisher information theory, agents generate information seeking command sequences. As an outcome, the swarm continuously optimizes its trajectory so that the agents’ position and orientation uncertainty is actively minimized. The proposed algorithm is verified by large-scale swarm simulations and demonstrated in a space-analogue mission of autonomous swarm navigation on the volcano Mount Etna.