Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers

• URL: http://arxiv.org/abs/2510.03544v1
• Date: Fri, 03 Oct 2025 22:28:46 GMT
• Title: Agile Tradespace Exploration for Space Rendezvous Mission Design via Transformers
• Authors: Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico,
• Abstract summary: Spacecraft rendezvous enables on-orbit servicing and debris removal, forming the foundation for a scalable space economy.<n>This paper proposes a framework that can be used to design missions for a wide range of flight times.<n>The framework provides high-quality initial guesses that generalize to solutions in fewer iterations.
• Score: 22.891825351056823
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the underlying constraints are often highly nonconvex, and mission designers must balance accuracy (e.g., solving the full problem) with efficiency (e.g., convex relaxations), slowing iteration and limiting design agility. To address these challenges, this paper proposes an AI-powered framework that enables agile mission design for a wide range of Earth orbit rendezvous scenarios. Given the orbital information of the target spacecraft, boundary conditions, and a range of flight times, this work proposes a Transformer-based architecture that generates, in a single parallelized inference step, a set of near-Pareto optimal trajectories across varying flight times, thereby enabling rapid mission trade studies. The model is further extended to accommodate variable flight times and perturbed orbital dynamics, supporting realistic multi-objective trade-offs. Validation on chance-constrained rendezvous problems with passive safety constraints demonstrates that the model generalizes across both flight times and dynamics, consistently providing high-quality initial guesses that converge to superior solutions in fewer iterations. Moreover, the framework efficiently approximates the Pareto front, achieving runtimes comparable to convex relaxation by exploiting parallelized inference. Together, these results position the proposed framework as a practical surrogate for nonconvex trajectory generation and mark an important step toward AI-driven trajectory design for accelerating preliminary mission planning in real-world rendezvous applications.

Related papers

• Multi UAVs Preflight Planning in a Shared and Dynamic Airspace [4.802862639452379]
  We propose DTAPP-IICR: a Delivery-Time Aware Prioritized Planning method with Incremental and Iterative Conflict Resolution.<n>Our framework first generates an initial solution by prioritizing missions based on urgency.<n>It computes roundtrip trajectories using SFIPP-ST, a novel 4D single-agent planner.<n>On benchmarks with temporal NFZs, DTAPP-IICR achieves near-100% success with fleets of up to 1,000 UAVs.
  arXiv  Detail & Related papers  (2026-02-12T15:18:46Z)
• Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance [22.261628532402067]
  This study presents a reinforcement learning based framework to enhance adaptive collision avoidance in active debris removal missions.<n>Small satellites are increasingly adopted due to their flexibility, cost effectiveness, and maneuverability, making them well suited for dynamic missions such as ADR.<n>The framework integrates refueling strategies, efficient mission planning, and adaptive collision avoidance to optimize spacecraft rendezvous operations.
  arXiv  Detail & Related papers  (2026-02-04T21:49:20Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation [54.87964060934928]
  Vision-Language-Action (VLA) models confront critical barriers to real-world deployment, most notably catastrophic forgetting.<n>We propose the Narrowing of Trajectory VLA framework: a novel approach that narrows its focus to sparse trajectories.<n>NoTVLA achieves superior performance and generalization compared to pi0 while operating under two critical constraints.
  arXiv  Detail & Related papers  (2025-10-04T18:26:55Z)
• ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy [51.56484100374058]
  ASTREA is the first agentic system deployed on flight-heritage hardware (TRL 9) for autonomous spacecraft operations.<n>We integrate a resource-constrained Large Language Model (LLM) agent with a reinforcement learning controller in an asynchronous architecture tailored for space-qualified platforms.
  arXiv  Detail & Related papers  (2025-09-16T08:52:13Z)
• OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics [43.410962336636224]
  OrbitZoo is a versatile multi-agent RL environment built on a high-fidelity industry standard library.<n>It supports scenarios like collision avoidance and cooperative maneuvers, and ensures robust and accurate orbital dynamics.<n>It is validated against a real satellite constellation, Starlink, achieving a Mean Absolute Percentage Error (MAPE) of 0.16% compared to real-world data.
  arXiv  Detail & Related papers  (2025-04-05T12:44:21Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics.<n>This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• Differentiable Motion Manifold Primitives for Reactive Motion Generation under Kinodynamic Constraints [5.982922468400902]
  Differentiable Motion Manifold Primitives (DMMP) is a novel neural network architecture that encodes and generates continuous-time, differentiable trajectories.<n> Experiments on dynamic throwing with a 7-DoF robot arm demonstrate that DMMP outperforms prior methods in planning speed, task success, and constraint satisfaction.
  arXiv  Detail & Related papers  (2024-10-16T03:29:33Z)
• GEO satellites on-orbit repairing mission planning with mission deadline constraint using a large neighborhood search-genetic algorithm [2.106508530625051]
  This paper proposes a large neighborhood search-adaptive genetic algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning. In the many-to-many on-orbit repairing scenario, several servicing spacecrafts and target satellites are located in GEO orbits which have different inclination, RAAN and true anomaly. The mission objective is to find the optimal servicing sequence and orbit rendezvous time of every servicing spacecraft to minimize total cost of all servicing spacecrafts with all target satellites repaired.
  arXiv  Detail & Related papers  (2021-10-08T03:33:37Z)
• Time-Optimal Planning for Quadrotor Waypoint Flight [50.016821506107455]
  Planning time-optimal trajectories at the actuation limit of a quadrotor is an open problem. We propose a solution while exploiting the full quadrotor's actuator potential. We validate our method in real-world flights in one of the world's largest motion-capture systems.
  arXiv  Detail & Related papers  (2021-08-10T09:26:43Z)
• Constrained optimisation of preliminary spacecraft configurations under the design-for-demise paradigm [1.0205541448656992]
  Most mid-sized satellites currently launched and already in orbit fail to comply with the casualty risk threshold of 0.0001. Satellites manufacturers and mission operators need to perform a disposal through a controlled re-entry. This additional cost and complexity can be removed as the spacecraft is directly compliant with the casualty risk regulations.
  arXiv  Detail & Related papers  (2020-12-27T17:48:29Z)
• A Maximum Independent Set Method for Scheduling Earth Observing Satellite Constellations [41.013477422930755]
  This paper introduces a new approach for solving the satellite scheduling problem by generating an infeasibility-based graph representation of the problem. It is tested on a scenarios of up to 10,000 requested imaging locations for the Skysat constellation of optical satellites as well as simulated constellations of up to 24 satellites.
  arXiv  Detail & Related papers  (2020-08-15T19:32:21Z)
• First Steps: Latent-Space Control with Semantic Constraints for Quadruped Locomotion [73.37945453998134]
  Traditional approaches to quadruped control employ simplified, hand-derived models. This significantly reduces the capability of the robot since its effective kinematic range is curtailed. In this work, these challenges are addressed by framing quadruped control as optimisation in a structured latent space. A deep generative model captures a statistical representation of feasible joint configurations, whilst complex dynamic and terminal constraints are expressed via high-level, semantic indicators. We validate the feasibility of locomotion trajectories optimised using our approach both in simulation and on a real-worldmal quadruped.
  arXiv  Detail & Related papers  (2020-07-03T07:04:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.