arXiv AI
Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
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arXiv AI
Oct 31
Where Planning Changes the Outcome of Orbital Games
The paper introduces OrbitalGym, a JAX‑based simulation environment for studying how planning algorithms and system design influence the outcomes of adversarial orbital encounters. It supports multiple agents, diverse state representations, communication modes, and sensor models, and compiles games to batched GPU code for large‑scale parallel simulation. Using OrbitalGym, the authors evaluate several control policies (PD, LQR, MPC, MCTS) across two close‑range games—pursuit‑evasion and lady‑bandit‑guard—showing that propellant availability often dominates in pursuit‑evasion, while policy choice significantly improves defense in the lady‑bandit‑guard scenario.
arXiv AI
5d ago
Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning
arXiv:2610.41647v1 Announce Type: new Abstract: Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome vari...
arXiv AI
Sep 15
Chance-Constrained Belief-Space Maneuver Planning for Autonomous Collision Avoidance Under Uncertainty
The paper presents a chance-constrained belief-space planning framework for autonomous collision avoidance in low Earth orbit. It models uncertain orbital states as Gaussian beliefs and uses a Monte Carlo tree search to decide whether to wait for better tracking data or to execute a maneuver before the time of closest approach. Experiments on 96 scenarios from NASA’s dataset show that the planner can avoid maneuvers in about 40% of cases while keeping collision risk below the threshold, with performance heavily dependent on tracking quality and cadence.
arXiv Machine Learning
Aug 11
Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).
arXiv Machine Learning
Oct 3
Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers
The paper introduces Reachability Analysis-Informed Reinforcement Learning (RARL) for designing deterministic multi‑impulse interplanetary transfers. RARL uses local first‑order reachability maps to bound velocity perturbations and selects intermediate waypoints, which are then translated into maneuvers via Lambert reconstruction and a terminal two‑impulse solution. Numerical experiments on an Earth‑Mars benchmark show that RARL achieves a mean maneuver cost only 1.72% above a validated convex programming reference and can be trained once to handle a wide range of departure states, achieving 100% feasibility on 10,000 held‑out Monte Carlo departures.
arXiv Computer Vision
Sep 22
DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models
arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expe...