Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles

As spacecraft rendezvous and docking missions become increasingly complex, the need for advanced solutions has surged. In recent years, the application of reinforcement learning techniques to tackle spacecraft rendezvous guidance challenges has emerged as a prominent international trend. Vital to ensuring the secure rendezvous and docking of spacecraft is the task of obstacle avoidance. However, traditional reinforcement learning algorithms lack the ability to enforce safety constraints within the exploration space, which presents a formidable obstacle in the design of spacecraft rendezvous guidance strategies. In response to this challenge, a spacecraft rendezvous guidance methodology founded on safety reinforcement learning is proposed. Firstly, a Markov model is crafted for autonomous spacecraft rendezvous in scenarios involving collision avoidance. A reward system, contingent on obstacle warnings and collision avoidance constraints, is introduced to establish a safety reinforcement learning framework for devising spacecraft rendezvous guidance strategies. Secondly, within the framework of safety reinforcement learning, two deep reinforcement learning (DRL) algorithms, Proximal Policy Optimisation (PPO) and Deep Deterministic Policy Gradient (DDPG), are leveraged to generate these guidance strategies. Experimental findings validate the effectiveness of this approach in successfully executing obstacle avoidance and achieving rendezvous with remarkable precision. Furthermore, through an analysis of the performance and generalization capabilities of these two algorithms, the efficacy of the proposed methodology is further underscored. This fusion of advanced space guidance technology with the principles of Ubiquitous Learning marks a significant step forward in the quest for safer and more efficient spacecraft rendezvous and docking operations.