# Guaranteed Safe Spacecraft Docking with Control Barrier Functions

arXiv: https://arxiv.org/abs/2109.07027 · HTML: https://mlsanity.com/paper/2109.07027/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles](https://mlsanity.com/paper/2405.06770/) (2024)
- [Learning Ensembles of Vision-based Safety Control Filters](https://mlsanity.com/paper/2412.02029/) (2024)
- [Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection](https://mlsanity.com/paper/2406.11795/) (2024)
- [Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning](https://mlsanity.com/paper/2510.21560/) (2025)
- [SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking](https://mlsanity.com/paper/2609.06061/) (2026)

## Abstract (arXiv)

This paper presents a strategy for control of a spacecraft docking with a non-maneuvering target in the presence of safety constraints and bounded disturbances. The presence of disturbances prevents convergence to a unique docking state, so in our formulation, docking is defined as occurring within a set constructed using prescribed tolerances. Safety is ensured via application of Robust Control Barrier Functions to render a designated safe set forward invariant for any allowable disturbance. However, this safety strategy necessarily presumes a worst-case disturbance, and thus restricts trajectories to a subset of the safe set when a worst-case disturbance is not present. The presented controller accounts for this restriction, and guarantees that the spacecraft both remains safe and achieves docking in finite time for any allowable disturbance. The controller is then validated in simulation for a spacecraft landing on an asteroid, and two spacecraft docking in low Earth orbit.