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Published July 22, 2019 | Accepted Version
Conference Paper Open

Distributed multi-target relative pose estimation for cooperative spacecraft swarm


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.

Additional Information

This research was supported in part by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The work of Kai Matsuka was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE 1745301. The work of Rebecca Foust was supported by a NASA Space Technology Research Fellowship (Grant No. NNX15AP48H). Also, we would like to thank Aaron Feldman and Jennifer Sun for their support on the robotic experiments.

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Accepted Version - IWSCFF2019_DPE__1_.pdf


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August 19, 2023
October 20, 2023