- The paper introduces a reinforcement learning framework that autonomously directs collision avoidance maneuvers using a Markov Decision Process.
- It employs a cross-entropy method and Lambert’s solution for initialization, achieving docking within a single orbit cycle despite increased fuel consumption.
- The approach demonstrates improved decision-making efficiency and lays groundwork for scaling to multiple servicers and targets in dynamic LEO environments.
Autonomous Decision Making for On-orbit Servicing in Spacecraft Collision Avoidance
The paper by Patnala and Abdin addresses a crucial aspect of space operations—spacecraft collision avoidance—by proposing an autonomous decision-making framework for on-orbit servicing (OOS). Their research leverages reinforcement learning (RL) to enhance the precision and reliability of collision avoidance maneuvers (CAMs), a pressing requirement in increasingly congested Low Earth Orbit (LEO) environments.
Methodology
The authors introduce a comprehensive mission architecture designed for OOS, where a servicer spacecraft autonomously assists a targeted satellite to execute collision avoidance maneuvers. Within this architecture, the servicer maintains a consistent orbit while monitoring potential collision scenarios using a predictive simulator based on Keplerian orbital dynamics. The system's decision-making is framed as a Markov Decision Process (MDP), which guides actions taken by the servicer based on its current state and the immediate environmental factors.
The training model for the system is built upon a cross-entropy (CE) method, known for its stochastic optimization capabilities. In this context, the CE method fine-tunes the sequence of maneuvers needed for effective CAM and docking operations. The action-value function drives the selection of maneuvers, with an objective to maximize the total reward, which includes factors such as collision probability, fuel efficiency, and trajectory deviation.
Results
In evaluating their approach, the authors perform a case paper simulating conditions with one servicer and one target satellite. They compare two initialization strategies for the training process: random initialization and initialization based on Lambert’s solution. Their analyses reveal that Lambert's solution fosters more efficient docking and CAM operations, with the servicer executing maneuvers more expeditiously compared to random initialization, albeit with higher fuel consumption.
The noteworthy numerical results include achieving docking within a single orbit cycle using Lambert's initialization. This reflects the framework's capability to autonomously drive the servicer's decision-making processes in dynamic orbital environments. Despite Lambert's method being more computationally demanding due to fine-grain calculation required near docking, it offers a distinct advantage in time efficiency.
Discussion and Implications
The findings from this paper underscore the potential of RL frameworks in transforming OOS missions, particularly in enhancing spacecraft's autonomous capabilities for collision avoidance. The methodological robustness exhibited suggests that such models can efficiently manage real-world unpredictability inherent in space operations. Moreover, the paper opens avenues for further exploration, including extending the capabilities of the framework to handle multiple servicers and targets simultaneously, thus broadening the scope of autonomous orchestration in satellite constellations.
Future prospects involve refining these models to incorporate aspects like longer time horizons in decision planning and integration of diverse environmental variables. As autonomous systems continue to evolve, the implications for not only space safety but also operational efficiency and sustainability become increasingly significant. As the landscape of space traffic continues to grow, the advancement and implementation of AI-driven strategies like those outlined in this research will underpin future developments in autonomous space operations.