On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making

• URL: http://arxiv.org/abs/2409.17125v1
• Date: Wed, 25 Sep 2024 17:40:37 GMT
• Title: On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making
• Authors: Susmitha Patnala, Adam Abdin,
• Abstract summary: This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs) We propose an autonomous servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study develops an AI-based implementation of autonomous On-Orbit Servicing (OOS) mission to assist with spacecraft collision avoidance maneuvers (CAMs). We propose an autonomous `servicer' trained with Reinforcement Learning (RL) to autonomously detect potential collisions between a target satellite and space debris, rendezvous and dock with endangered satellites, and execute optimal CAM. The RL model integrates collision risk estimates, satellite specifications, and debris data to generate an optimal maneuver matrix for OOS rendezvous and collision prevention. We employ the Cross-Entropy algorithm to find optimal decision policies efficiently. Initial results demonstrate the feasibility of autonomous robotic OOS for collision avoidance services, focusing on one servicer spacecraft to one endangered satellite scenario. However, merging spacecraft rendezvous and optimal CAM presents significant complexities. We discuss design challenges and critical parameters for the successful implementation of the framework presented through a case study.

Related papers

• AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions [0.0]
  Debris in Low Earth Orbit represents a significant threat to space sustainability and spacecraft safety. Armoured Transfer Vehicles (OTVs) facilitate debris deorbiting, thereby reducing future collision risks. Armoured decision-planning model based on Deep Reinforcement Learning (DRL) is developed to train an OTV to plan optimal debris removal sequencing. It is shown that using the proposed framework, the agent can find optimal mission plans and learn to update the planning autonomously to include risk handling of debris with high collision risk.
  arXiv  Detail & Related papers  (2024-09-25T15:16:07Z)
• Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach [0.0]
  This work proposes an implementation of autonomous CA decision-making capabilities on spacecraft based on Reinforcement Learning techniques. The proposed framework considers imperfect monitoring information about the status of the debris in orbit and allows the AI system to effectively learn policies to perform accurate Collision Avoidance Maneuvers (CAMs) The objective is to successfully delegate the decision-making process for autonomously implementing a CAM to the spacecraft without human intervention.
  arXiv  Detail & Related papers  (2023-10-29T10:15:33Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• SpaceYOLO: A Human-Inspired Model for Real-time, On-board Spacecraft Feature Detection [0.0]
  Real-time, automated spacecraft feature recognition is needed to pinpoint the locations of collision hazards. New algorithm SpaceYOLO fuses a state-of-the-art object detector YOLOv5 with a separate neural network based on human-inspired decision processes. Performance in autonomous spacecraft detection of SpaceYOLO is compared to ordinary YOLOv5 in hardware-in-the-loop experiments.
  arXiv  Detail & Related papers  (2023-02-02T02:11:39Z)
• Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System [0.0]
  The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems. Limitations in the currently mandated TCAS led the Federal Aviation Administration to commission the development of a new solution, the Airborne Collision Avoidance System X (ACAS X) This work explores the benefits of using a DRL collision avoidance system whose parameters are tuned using a surrogate.
  arXiv  Detail & Related papers  (2022-12-01T20:20:41Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• 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)
• Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions [77.34726150561087]
  This paper investigates the use of reinforcement learning for the robust design of interplanetary trajectories in presence of severe disturbances. An open-source implementation of the state-of-the-art algorithm Proximal Policy Optimization is adopted. The resulting Guidance and Control Network provides both a robust nominal trajectory and the associated closed-loop guidance law.
  arXiv  Detail & Related papers  (2020-08-19T15:22:15Z)
• Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks [51.05735925326235]
  A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. We study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique.
  arXiv  Detail & Related papers  (2020-05-26T05:39:27Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

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.