Neural operator learning for collision-aware trajectory planning of spacecraft swarms
A permutation-equivariant neural operator maps distributions of spacecraft, targets, and debris to collision-aware trajectories for an entire swarm in a single forward pass Training is fully self-supervised, combining physics objectives with adversarial threats generated against the model's own rollouts, requiring no optimal-trajectory labels The model trained on 10 spacecraft generalizes zero-shot to swarms of 1,000 amid 11,000+ catalogued debris objects, matching per-agent optimal-control solv
Analysis
TL;DR
- A permutation-equivariant neural operator maps distributions of spacecraft, targets, and debris to collision-aware trajectories for an entire swarm in a single forward pass
- Training is fully self-supervised, combining physics objectives with adversarial threats generated against the model's own rollouts, requiring no optimal-trajectory labels
- The model trained on 10 spacecraft generalizes zero-shot to swarms of 1,000 amid 11,000+ catalogued debris objects, matching per-agent optimal-control solver accuracy
- A batched Gauss-Newton finish enforces exact orbital dynamics, ensuring physical feasibility of generated trajectories
- The approach significantly reduces intra-swarm proximity and evades worst-case adversarial threats that debris-blind baselines cannot handle
Why It Matters
This work addresses a critical scalability bottleneck in autonomous space operations: classical trajectory optimization becomes intractable as pairwise safety constraints grow quadratically with swarm size, while existing learning-based planners fail to transfer across different swarm configurations and debris densities. For AI practitioners working in safety-critical multi-agent systems, this demonstrates how physics-grounded neural operators can achieve both generalization and exact constraint enforcement simultaneously.
Technical Details
- Architecture: Permutation-equivariant neural operator that takes as input the joint distribution of spacecraft positions, target states, and debris catalog, and outputs collision-aware trajectories for all agents in a single forward pass
- Training paradigm: Fully self-supervised without optimal-trajectory labels; combines physics-based objectives (orbital dynamics compliance) with adversarial training where threats are generated against the model's own rollouts
- Post-processing: Batched Gauss-Newton finisher enforces exact orbital dynamics constraints, bridging the gap between learned approximations and physically feasible solutions
- Scale and generalization: Trained on 10 spacecraft, zero-shot tested on swarms of 1,000 agents operating among 11,000+ catalogued debris objects
- Performance: Matches per-agent optimal-control solver accuracy while reducing intra-swarm proximity several-fold and successfully evading worst-case adversarial threats
Industry Insight
- Physics-grounded operator learning represents a viable path toward real-time multi-agent planning in high-dimensional constraint spaces, applicable beyond aerospace to autonomous vehicle coordination and robotic swarms
- The adversarial self-training approach—generating threats against one's own rollouts—offers a general recipe for building robustness in safety-critical planning systems without requiring labeled optimal solutions
- As orbital congestion intensifies, scalable collision-aware planning will become a hard requirement for any large-scale space mission; this work establishes a new benchmark for zero-shot generalization across agent counts and environmental densities
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