Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations

• URL: http://arxiv.org/abs/2606.07580v1
• Date: Tue, 26 May 2026 21:14:00 GMT
• Title: Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations
• Abstract summary: An active tether-net system with maneuverable units is one of the promising solutions to this problem.<n>This paper focuses on the first of these two uncertainty sources, and presents a pipeline to propagate and quantify the resulting uncertainty.<n>A high-fidelity simulator and a lower-fidelity surrogate-based environment are used to demonstrate trade-offs between prediction accuracy versus ease of resolving uncertainties.
• Score: 8.825977169303206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Low Earth Orbit has grown more crowded with space debris, the need for reliable and efficient debris removal solutions becomes more urgent. An active tether-net system with maneuverable units is one of the promising solutions to this problem, whose success is dependent on the robustness of the net maneuver and closing decisions. These in turn are impacted by the uncertainties attributed to i) noisy observation of the target debris state (e.g., sensing errors), and ii) imperfect simulations of the complex net dynamics and net/debris interaction behavior, over which the decision system is trained. This paper focuses on the first of these two uncertainty sources, and presents a pipeline to propagate and quantify the resulting uncertainty in the debris capture performance expressed in terms of Capture Quality Index (CQI). This quantification is uniquely performed for both an active tether-net using a fixed baseline control and one using a trained neuro-control policy to guide the net maneuver during the deployment phase. Two different uncertainty quantification (UQ) techniques, namely Sobol's variance-based sensitivity analysis and perturbation-based method are exploited. A high-fidelity simulator and a lower-fidelity surrogate-based environment are used to demonstrate trade-offs between prediction accuracy versus ease of resolving uncertainties.

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