{"slug": "neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft", "title": "Neural operator learning for collision-aware trajectory planning of spacecraft swarms", "summary": "Researchers introduced a permutation-equivariant neural operator for collision-aware trajectory planning of spacecraft swarms, trained on ten spacecraft and generalizing zero-shot to swarms of 1,000 amid more than 11,000 catalogued objects, matching a per-agent optimal-control solver's accuracy and evading worst-case threats. The method, detailed in arXiv:2608.00320v1, offers a fast, scalable alternative to optimal control for crowded orbits.", "body_md": "arXiv:2608.00320v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft", "canonical_source": "https://arxiv.org/abs/2608.00320", "published_at": "2026-08-04 04:00:00+00:00", "updated_at": "2026-08-04 04:34:36.585728+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "neural-networks", "ai-research"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft", "markdown": "https://wpnews.pro/news/neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft.md", "text": "https://wpnews.pro/news/neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft.txt", "jsonld": "https://wpnews.pro/news/neural-operator-learning-for-collision-aware-trajectory-planning-of-spacecraft.jsonld"}}