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Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning — arXiv:2610.41639

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41639
License (as archived): CC0-1.0
Archived: 2026-11-02T07:39:28.340Z
SHA-256 of the archived text: e6648c3a3a5fd6a0a6baf5329cac6e074102a3690f5e983295dca524b84fceb8
Stable URL of this page: https://metraipolis.com/documento/dc_fb82eebd040e

How to cite

Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning — arXiv:2610.41639 — arXiv (en), https://arxiv.org/abs/2610.41639 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb82eebd040e (Metraipolis archive dc_fb82eebd040e, sha256 e6648c3a3a5fd6a0a6baf5329cac6e074102a3690f5e983295dca524b84fceb8)

BibTeX

@misc{dcfb82eebd040e,
  title = {Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning — arXiv:2610.41639},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41639},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb82eebd040e; sha256 e6648c3a3a5fd6a0a6baf5329cac6e074102a3690f5e983295dca524b84fceb8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41639. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints involves a complex combinatorial problem constrained by coverage, fuel, and safety considerations. This paper models the viewpoint selection as a Constrained Markov Decision Process (CMDP) and applies Proximal Policy Optimization with Lagrangian relaxation to solve it. The system combines a high-level planner, which sequences camera viewpoints on an observation sphere around the target, with a low-level model that assesses each transfer using the Clohessy--Wiltshire (CW) equations for relative motion. The deterministic constraints on single-impulse reachability and $\Delta v$ cap are enforced directly on the action set. Safety margins are affected by navigation and manoeuvre errors, making the actual clearance stochastic. To address this, a chance constraint on the realised trajectory is implemented, with a Lagrangian multiplier governed by PID control to adapt the penalty during training. The approach is tested on a Galileo-class navigation satellite in medium Earth orbit, modelled as a point cloud where visibility from each viewpoint considers field-of-view and line-of-sight occlusion. Across various risk budgets, the trained policy maintains the keep-out violation rate within the specified limit, achieving coverage close to its maximum and staying within the mission $\Delta v$ budget. Compared to the usual practice of applying a fixed margin to the keep-out radius and planning deterministically, the chance-constrained policy attains less than half the violation rate at comparable coverage. This makes the collision risk an explicit design parameter, eliminating the need for re-tuning per case. The learned policy selects a viewpoint without the repeated Monte Carlo risk estimates required by a greedy, chance-constrained planner at every decision point.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Optimization and Control (math.OC).
Pagina del lavoro: https://arxiv.org/abs/2610.41639 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:37:00.497Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).