Autonomous Six-Degree-of-Freedom Spacecraft Docking with Rotating Targets via Reinforcement Learning

Analysis

Why This Paper Matters

This paper addresses a critical challenge in space operations: autonomous docking with rotating targets. As satellite servicing and orbital debris removal become increasingly important, the ability to perform six-degree-of-freedom docking maneuvers without human intervention is essential. Traditional optimal control methods often require precise models and high computational resources, limiting their applicability in real-time, uncertain space environments. By leveraging reinforcement learning, the authors offer a path toward low-cost, robust onboard control that can adapt to target rotation and other disturbances.

The use of proximal policy optimization (PPO) is particularly significant because it balances sample efficiency and stability, making it suitable for training policies in high-dimensional continuous state spaces like spacecraft dynamics. The paper also provides a direct comparison with optimal control techniques, grounding the RL approach in established aerospace practice. This work thus serves as a bridge between modern machine learning and classical astrodynamics, opening new avenues for autonomous proximity operations.

Technical Contributions

The paper's key innovations include:

• Six-DOF docking policy: The RL policy controls both translational and rotational degrees of freedom, a significant step beyond simpler planar or attitude-only maneuvers.
• PPO for spacecraft control: Demonstrates that PPO can learn effective docking maneuvers despite the complex, nonlinear dynamics and the need for precise terminal alignment.
• Rotating target handling: The policy explicitly accounts for targets spinning about a constant axis, a realistic scenario for many space objects.
• Apollo scenario validation: Using the historic Apollo transposition and docking maneuver with an induced spin provides a concrete, reproducible benchmark.
• Cost minimization: The reward function is designed to minimize both performance error and control effort, aligning with practical mission constraints.

Results

The experiments show that the RL policy successfully docks the chaser spacecraft with a rotating lunar module in simulation. The policy achieves docking with low control costs and performance metrics comparable to those from standard optimal control techniques. While exact numerical metrics (e.g., success rate, docking time, fuel consumption) are not detailed in the abstract, the paper claims the policy is valid over a portion of the state space and provides a foundation for further research. The comparison with optimal control highlights that RL can match traditional methods while offering greater robustness to model uncertainties.

Significance

This research has broad implications for the AI and aerospace communities. It demonstrates that reinforcement learning can be applied to safety-critical, high-dimensional control problems in space, where reliability and computational efficiency are paramount. The work encourages further investigation into learning-based control laws for proximity operations, potentially reducing the need for extensive ground-based computation and enabling autonomous missions to service satellites or remove debris. By openly discussing challenges and workarounds, the authors also help guide future researchers in applying RL to real-world aerospace systems.