Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous

2024-09-25 · Agni Bandyopadhyay, Guenther Waxenegger-Wilfing

This research introduces a novel application of a masked Proximal Policy Optimization (PPO) algorithm from the field of deep reinforcement learning (RL), for determining the most efficient sequence of space debris visitation, utilizing the Lambert solver as per Izzo's adaptation for individual rendezvous. The aim is to optimize the sequence in which all the given debris should be visited to get the least total time for rendezvous for the entire mission. A neural network (NN) policy is developed, trained on simulated space missions with varying debris fields. After training, the neural network calculates approximately optimal paths using Izzo's adaptation of Lambert maneuvers. Performance is evaluated against standard heuristics in mission planning. The reinforcement learning approach demonstrates a significant improvement in planning efficiency by optimizing the sequence for debris rendezvous, reducing the total mission time by an average of approximately {10.96\%} and {13.66\%} compared to the Genetic and Greedy algorithms, respectively. The model on average identifies the most time-efficient sequence for debris visitation across various simulated scenarios with the fastest computational speed. This approach signifies a step forward in enhancing mission planning strategies for space debris clearance.

📄 PDF Abstract BibTeX arXiv:2409.16882

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System

2026-05-25 · Yuhui Hu, Dong Zhou, Kaihong Ouyang, Zhongliang Yu 외 arxiv

The strong dynamic coupling between the manipulator and the base poses a significant challenge to maintaining spacecraft attitude stability, potentially compromising mission safety. In this paper, we propose a Dual-Agent…

Reinforcement Learning

AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions

2024-09-25 · Antoine Poupon, Hugo de Rohan Willner, Pierre Nikitits, Adam Abdin

The proliferation of debris in Low Earth Orbit (LEO) represents a significant threat to space sustainability and spacecraft safety. Active Debris Removal (ADR) has emerged as a promising approach to address this issue, u…

Deep Reinforcement Learning Scheduling

Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning

2021-11-09 · Stefan Radic Webster, Peter Flach

Identifying uncertainty and taking mitigating actions is crucial for safe and trustworthy reinforcement learning agents, especially when deployed in high-risk environments. In this paper, risk sensitivity is promoted in …

Model-based Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance

2026-02-04 · Agni Bandyopadhyay, Gunther Waxenegger-Wilfing arxiv

As the orbital environment around Earth becomes increasingly crowded with debris, active debris removal (ADR) missions face significant challenges in ensuring safe operations while minimizing the risk of in-orbit collisi…

Reinforcement Learning Collision Avoidance

Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design

2021-03-16 · Yuji Takubo, Hao Chen, Koki Ho

This paper develops a hierarchical reinforcement learning architecture for multimission spaceflight campaign design under uncertainty, including vehicle design, infrastructure deployment planning, and space transportatio…

Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1