Launch and Closure Optimization under Uncertainties for a Tether-Net Space Debris Capture System

View Video Presentation: https://doi.org/10.2514/6.2021-3103.vid Tether-nets deployed from a chaser spacecraft have been proposed as a promising technical solution to capture space debris in recent years. The success of this (usually one-shot) process however depends on the ability to perform an optimal launch and closure of the tether-net, subject to uncertainties. Uncertainties are primarily attributed to the configuration of the net, inadequacies in modeling the dynamics of the tether-net, inexact sensing and state estimation of the space debris and inexact actuation at launch and closing stages. This paper presents a design optimization framework to tackle the sources of uncertainties in optimizing the process parameters associated with: 1) launch: i.e., launch velocities, and 2) closure: i.e., the distance to the target when the closing mechanism is actuated. In the absence of real data, Gaussian noises are applied to model the uncertainties in tether net dynamics, state estimation and actuation, meanwhile Monte Carlo (MC) sampling is utilized to capture the propagation of these uncertainties. Performance is quantified in terms of success rate of capture, given by the Capture Quality Index, estimation of which becomes noisy due to the MC modeled uncertainties. This, along with the significant computational expense of simulating the tether-net dynamics (using the Vortex software), motivated us to use Bayesian Optimization in this framework. Two case studies are performed to compare/contrast performance when only launch velocities are optimized vs. when both launch velocities and closing distance are simultaneously optimized. given the greater degree of control freedom, the second case expectedly provided a 99 % probability of success, compared to 80 % with the first case. Further analysis of the sensitivity of the optimized tether net process to the uncertainty specifications showed that launch direction had the maximum impact.