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AI-Driven Risk-Aware Scheduling for Active Debris Removal Missions
Artificial Intelligence · Submitted 25 Sep 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. In this paper, an autonomous 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The proliferation of debris in Low Earth Orbit (LEO) represents a significant threat to space sustainability and spacecraft safety. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Active Debris Removal (ADR) has emerged as a promising approach to address this issue, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. | background 1.00 | 0.05 | 0.03 | asserted 0.93 |
| s2 | However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. | background 0.91 | 0.05 | 0.02 | asserted 1.00 |
| s3 | Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. | background 0.87 | 0.05 | 0.02 | asserted 1.00 |
| s4 | In this paper, an autonomous decision-planning model based on Deep Reinforcement Learning (DRL) is developed to train an OTV to plan optimal debris removal sequencing. | method 0.55 | 0.95 | 0.10 | asserted 1.00 |
| s5 | 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. | result 0.99 | 0.93 | 0.87 | asserted 1.00 |
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The paper
Original abstract
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, utilising Orbital Transfer Vehicles (OTVs) to facilitate debris deorbiting, thereby reducing future collision risks. However, ADR missions are substantially complex, necessitating accurate planning to make the missions economically viable and technically effective. Moreover, these servicing missions require a high level of autonomous capability to plan under evolving orbital conditions and changing mission requirements. In this paper, an autonomous 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.
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What this paper does with the arXiv papers it cites
What it cites · 1 sentence without a confident label
Uses the method or tool of 2 papers
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As discussed in [ref] , a Fixed Q-Target approach was used to stabilise the learning.
Playing Atari with Deep Reinforcement Learning · DQN Agent · 0.98
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Hyperparameter tuning of the agent was undertaken on this dataset using Bayesian Optimisation [ref] .
Practical Bayesian Optimization of Machine Learning Algorithms · Results · 1.00
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