用神经算子实现千艘航天器群的快速避障轨迹规划
Neural operator learning for collision-aware trajectory planning of spacecraft swarms
- 设计可置换的神经算子，单次前向传播生成全群轨迹
- 零样本泛化到1000艘航天器，避障成功率远超传统方法
- 无需最优解标签，通过自监督和对抗训练提升鲁棒性
自主航天器群需在日益拥挤的轨道中规划节能且无碰撞的机动路径，但经典轨迹优化随群组规模增大而计算成本激增，学习型规划器通常无法跨规模或碎片密度迁移。本文提出一种排列等变神经算子，将航天器、目标及碎片分布映射为整个群组的避碰轨迹，配合批处理高斯-牛顿优化以精确满足轨道动力学。该算子无需最优轨迹标签，结合自监督物理目标与针对自身预测结果生成的对抗威胁进行训练。在10艘航天器上训练后，可零样本推广至含超过11,000个已编目物体的1,000艘航天器群，精度媲美逐代理最优控制求解器，有效规避了碎片盲视基线无法应对的最坏情况威胁，且群内近距离显著降低数倍。基于物理的神经算子学习为密集轨道提供了快速可扩展的替代方案。
原文摘要 · Abstract (English)
Autonomous spacecraft swarms must plan fuel-efficient, collision-free maneuvers in increasingly congested orbits, yet classical trajectory optimization scales poorly as pairwise safety constraints multiply with swarm size, and learning-based planners rarely transfer across swarm sizes or debris densities. Here we introduce a permutation-equivariant neural operator that maps distributions of spacecraft, targets and debris to collision-aware trajectories for an entire swarm in a single forward pass, paired with a batched Gauss-Newton finish that enforces exact orbital dynamics. The operator is trained without optimal-trajectory labels, combining self-supervised physics objectives with adversarial threats generated against its own rollouts. Trained on ten spacecraft, it generalizes zero-shot to swarms of 1,000 amid more than 11,000 catalogued objects, matching a per-agent optimal-control solver's accuracy, evading worst-case threats that a debris-blind baseline cannot, and reducing proximity within the swarm several-fold. Physics-grounded operator learning thus offers a fast, scalable alternative to optimal control for crowded orbits.
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