Laboratory Testbed Development for Modeling Spacecraft Proximity Operations

Abstract

Proximity operations, such as rendezvous, proximity maneuvering, and docking procedures are difficult to conduct between two spacecrafts in orbit. This is due to no longer understanding the dynamics of either system fully, as they may have changed in the time since leaving the surface of Earth. To combat these challenges, while ensuring mission success in the form of constraint enforcement and efficient procedures, machine learning via a Learning Reference Governor (LRG) is being investigated to perform these tasks without relying on a dynamic model of the spacecraft during the mission. For such developing technological advancements, testing and proof of concept must be completed. To do this a laboratory testbed is being developed to model spacecraft proximity operations on omni-directional robots by using their ground-based data to simulate docking and rendezvous in the orbital frame. This development has involved the construction of four omni-directional robots, and a systemic configuration that involves the repeated scaling of data from the lab frame to the orbital frame, implementing control and guidance algorithms, reverse scaling, and communicating between the robots and lab’s VICON system for state tracking and updating. Initial testing involved an LQR controller and a circular orbit around Earth, however this testbed is being designed so it can test a variety of controllers and orbits in future work, specifically the LRG. This report is on the current state of this testbed development and its potential capabilities.