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This paper describes the second iteration in the development of a simulation tool that provides trade-offs of avionic architectures for future Active Debris Removal (ADR) space missions. Challenges of ADR missions lay in their ability to first detect and track a target, then perform proximity operation and capture. All these mission phases imply a variety of sensors which are mainly needed for the Guidance, Navigation and Control (GNC) of the spacecraft. First, sensors outputs need to be processed to retrieve position and attitude estimation of the target, then results are transmitted to the GNC algorithms for precise navigation. To obtain accurate target information, the algorithms require a high input data rate and multiple sensor sources. The first one guarantees constant tracking and the second prevents any biasing. Furthermore, due to orbital mechanism and potential low ground coverage, data have to be analyzed on-board to satisfy a constant feed of the algorithms. A new version of the simulation tool has been developed to help design the high demanding avionic of the ADR mission, ClearSpace-1 (CS-1). It is designed to optimize the resources management inside the architecture. The simulator has to decide the set of instruments to use in conjunction with the types of algorithm to deploy. The goal is to find the optimal set that satisfies both the physical constrains such as available power and limit in the processing resources as well as requirements of the mission with mainly the accuracy on target tracking. This problem is solved using an Optimal Control (OC) approach with a Mixed Integer NonLinear Programming (MINLP) definition. The EPFL Space Center is using this tool to investigate multiple hardware configurations with specific ADR requirements for ClearSpace-1. It outputs which sensors to use during which time interval as well as the general hardware and software configuration. The optimizer part allows creation of preliminary scenario plans and helps in setting the requirements for the design of the high performance avionic.
Zoë Holmes, Joseph Richard Gibbs
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Colin Neil Jones, Yingzhao Lian, Loris Di Natale, Jicheng Shi, Emilio Maddalena