Distributed multi-target relative pose estimation for cooperative spacecraft swarm

Abstract

Multi-agent relative state estimation is critical in enabling full swarm autonomy. However, relative pose estimation of hundreds to thousands of cooperative agents is challenging due to limited sensing, limited communication, and scalability. We present a distributed algorithm for cooperative multi-agent localization with both limited relative sensing and communication. Each agent locally exchanges the relative measurements and jointly estimates the relative poses of its local neighbors. Because the algorithm only estimates the local neighbors, the number of states does not grow with the total number of agents given the same local sensing and communication graphs, making the algorithm suitable for swarm application. The proposed algorithm is applied to spacecraft swarm localization and verified in simulation and experiments. Experiments are conducted on Caltech’s robotic spacecraft simulators, the Multi-Spacecraft Testbed for Autonomy Research (M-STAR), where each spacecraft uses vision-based relative measurements.