Task Allocation and Maneuver Planning Method of Multi-Target Flyby for Large-Scale Constellation

At present, the large-scale in-orbit deployment of constellations is steadily progressing. Both satellites and space debris pose a significant risk of chain-reaction collisions. Accurate flyby and orbit determination of space debris constitute the key technologies for addressing this issue. Regarding the problem of flyby maneuvering for multiple space debris by large-scale constellations in near-Earth orbit, an Improved Discrete Particle Swarm Optimization (IDPSO) algorithm based on multi-population parallel search has been proposed. The proposed method is applicable to the on-orbit servicing of large-scale constellations comprising thousands of satellites. This study introduces a coding mechanism that balances low computational complexity with scalability and designs a multi-population parallel mechanism adaptable to distributed programming. Additionally, a differential correction method is proposed to refine the initial solution of the Lambert problem under the assumption of no perturbation, enabling maneuver planning under J2 perturbation conditions. Illumination constraints are incorporated into the algorithm to ensure load separation and terminal-point observability. Through numerical simulation examples, the rationality of the proposed algorithm and the accuracy of the flyby planning algorithm have been validated. This research integrates task allocation architecture with swarm intelligence optimization strategies, offering a highly robust and low-dependency solution for near-Earth space servicing systems.