Guaranteed Safe Spacecraft Docking with Control Barrier Functions
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Sep 2021.
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Citing papers per year: 2024: 3; 2025: 1; 2026: 1.
5 papers.
- 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehiclescited by 2
- 2024Learning Ensembles of Vision-based Safety Control Filterscited by 2
- 2024Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspectioncited by 1
- 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learningcited by 1
- 2026SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Dockingcited by 0
- 2026SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking5 Sep 2026
- 2025Learning Neural Control Barrier Functions from Expert Demonstrations using Inverse Constraint Learning24 Oct 2025
- 2024Learning Ensembles of Vision-based Safety Control Filters2 Dec 2024
- 2024Run Time Assured Reinforcement Learning for Six Degree-of-Freedom Spacecraft Inspection17 Jun 2024
- 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles10 May 2024