arXiv (All)
open access
Low-Thrust Trajectory Optimization with Quantum Computing and Sequential Convex Programming
Low-thrust trajectory optimization is a central task in interplanetary mission design, but its nonlinear dynamics and operational constraints often lead to challenging non-convex optimal-control problems. Sequential convex programming has emerged as an effective approach to address these problems, w
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning#361463
- Hybrid Reinforcement Learning and Search for Flight Trajectory Planning#635637
- Hybrid Reinforcement Learning and Search for Flight Trajectory Planning#633288
- A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking#629844
- A Diffusion-Refined Planner with Reinforcement Learning Priors for Confined-Space Parking#627982
Record · ID 820863
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive