面向航天器集群碰撞感知轨迹规划的神经算子学习
Neural Operator Learning for Collision-Aware Trajectory Planning of Spacecraft Swarms
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中文总结 AI 辅助
该研究提出排列等变神经算子，结合自监督物理目标与对抗威胁训练，实现航天器集群轨迹规划，可零样本泛化到大规模集群，精度接近最优控制求解器，性能优于无碎片感知基线。
中文摘要 AI 辅助
自主航天器集群需在日益拥挤的轨道中规划燃料高效、无碰撞的机动，但经典轨迹优化方法随着集群规模增大，成对安全约束数量激增，扩展性差；而基于学习的规划器很少能跨集群规模或碎片密度迁移。本文提出一种排列等变神经算子，可在单次前向传播中映射航天器、目标及碎片的分布，生成整个集群的碰撞感知轨迹，搭配批处理高斯-牛顿收尾环节，确保轨道动力学精确性。该算子无需最优轨迹标签即可训练，将自监督物理目标与针对自身 rollout 生成的对抗威胁相结合。在10个航天器上训练后，它可零样本泛化到含11000余颗 catalogued 物体的1000个航天器集群，达到每个智能体最优控制求解器的精度，规避无碎片感知基线无法应对的最坏情况威胁，将集群内接近度降低数倍。因此，基于物理的算子学习为拥挤轨道的最优控制提供了一种快速、可扩展的替代方案。
英文摘要
Satellite constellations require orbital transfers that are both fuel efficient and collision avoidant. Yet, the computational cost of optimization methods traditionally used to plan their trajectories scales poorly with both the number of satellites as well as the number of obstacles to avoid, due to the pairwise safety constraints. In this work, we introduce a permutation-equivariant neural operator for trajectory planning of spacecraft swarms. This neural operator maps distributions of spacecraft initial states, target states, and obstacle initial states to trajectories which avoid collision and conserve fuel. This neural operator output is then paired with a batched Gauss-Newton finish to enforce exact orbital dynamics, and further reduce fuel use. The operator is self-supervised, trained without optimal trajectory labels. When trained on ten spacecraft, the proposed method generalized zero-shot to swarms of 1,000 spacecraft and 11,000 obstacles. The generated trajectories matched a per-agent optimal control solver's accuracy while retaining collision avoidance. Operator learning grounded in physics may offer a fast, scalable alternative to trajectory optimization in the increasingly crowded orbits of the future.
发表机构
- Purdue University（普渡大学）
- Manifold Research Group（流形研究组）
机构由 AI 辅助整理，请以论文原文为准。
