arXiv Machine Learning August 4, 2026 By Sidhdharth D. Sikka, Suyi Gao, Zehui Lu, Rongjie Lai, Shaoshuai Mou

Neural operator learning for collision-aware trajectory planning of spacecraft swarms

Read the original on arXiv Machine Learning →

arXiv:2608. 00320v1 Announce Type: new Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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