DD
D. Du
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Advancing autonomous spacecraft proximity maneuvers and docking (PMD) is crucial for enhancing the efficiency and safety of inter-satellite services. One primary challenge in PMD is the accurate a priori definition of the system model, often complicated by inherent uncertainties in the system modeling and observational data. To address this challenge, we propose a novel Lyapunov Bayesian actor-critic reinforcement learning algorithm that guarantees the stability of the control policy under uncertainty. The PMD task is formulated as a Markov decision process that involves the relative dynamic model, the docking cone, and the cost function. By applying Lyapunov theory, we reformulate temporal difference learning as a constrained Gaussian process regression, enabling the state-value function to act as a Lyapunov function. Additionally, the proposed Bayesian quadrature policy optimization method analytically computes policy gradients, effectively addressing stability constraints while accommodating informational uncertainties in the PMD task. Experimental validation on a spacecraft air-bearing testbed demonstrates the significant and promising performance of the proposed algorithm.
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Advancing autonomous spacecraft proximity maneuvers and docking (PMD) is crucial for enhancing the efficiency and safety of inter-satellite services. One primary challenge in PMD is the accurate a priori definition of the system model, often complicated by inherent uncertainties in the system modeling and observational data. To address this challenge, we propose a novel Lyapunov Bayesian actor-critic reinforcement learning algorithm that guarantees the stability of the control policy under uncertainty. The PMD task is formulated as a Markov decision process that involves the relative dynamic model, the docking cone, and the cost function. By applying Lyapunov theory, we reformulate temporal difference learning as a constrained Gaussian process regression, enabling the state-value function to act as a Lyapunov function. Additionally, the proposed Bayesian quadrature policy optimization method analytically computes policy gradients, effectively addressing stability constraints while accommodating informational uncertainties in the PMD task. Experimental validation on a spacecraft air-bearing testbed demonstrates the significant and promising performance of the proposed algorithm.
Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control experiments, i.e., a 2D quadrotor navigation task. The experimental findings reveal this approach's effectiveness in reachability and safety, surpassing other model-free RL methods.
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Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control experiments, i.e., a 2D quadrotor navigation task. The experimental findings reveal this approach's effectiveness in reachability and safety, surpassing other model-free RL methods.