#### Session

Pre-Conference Workshop Session I: Advanced Concepts I

#### Location

Utah State University, Logan, UT

#### Abstract

One major obstacle to successful orbital debris remediation is the determination of which pieces of debris are the most viable targets for capture and de-orbit. The viability of a target is determined by some combination of the debris’ risk factor (a combination of its size, composition, andthe orbit it occupies), the anticipated resource cost to find and capture the debris, and the underlying probability of successful intercept and capture of that target. The problem of selecting debris for capture by a multi-capture capable spacecraft is fundamentally a traveling salesman problem in which the traveler only has the resources to reach a very limited subset of the available destinations. Therefore, rapidly identifying the sets of destinations (i.e. pieces of debris) which are either too expensive to reach or insufficiently valuable to justify targeting will reduce the target destination set; this would significantly enhance the efficiency of the solution. This problem of intelligently reducing the space of possible solutions can be partially solved by performing a preliminary filtering and sorting of orbital debris database entries using known spacecraft orbital parameters and maneuvering ∆V-budget to reduce the number of possible destinations for an optimizer to those which are in fact accessible from the spacecraft’s initial orbit. The chosen algorithm for analyzing and filtering the data is a two-burn node-to-node non-Hohmann transfer, which was used to estimate the ∆V-cost for transfer from the capture spacecraft’s initial orbit to an orbit near the target piece of debris. Once the ∆V-cost was calculated for each transfer orbit, entries with excessive fuel costs were removed from consideration, and the fuel cost to access each remaining orbit was appended to its entry. This method was capable of reducing a 10,400-item list of debris to less than 100 accessible targets in under 3 seconds on an ordinary laptop computer. This reduction in database size brought the number of targets down to a practical size for processing by a more computationally expensive optimization algorithm suitable for selecting final targets for a multi-capture spacecraft.

A Non-Hohmann Method for Orbital Element Database Pre-Processing

Utah State University, Logan, UT

One major obstacle to successful orbital debris remediation is the determination of which pieces of debris are the most viable targets for capture and de-orbit. The viability of a target is determined by some combination of the debris’ risk factor (a combination of its size, composition, andthe orbit it occupies), the anticipated resource cost to find and capture the debris, and the underlying probability of successful intercept and capture of that target. The problem of selecting debris for capture by a multi-capture capable spacecraft is fundamentally a traveling salesman problem in which the traveler only has the resources to reach a very limited subset of the available destinations. Therefore, rapidly identifying the sets of destinations (i.e. pieces of debris) which are either too expensive to reach or insufficiently valuable to justify targeting will reduce the target destination set; this would significantly enhance the efficiency of the solution. This problem of intelligently reducing the space of possible solutions can be partially solved by performing a preliminary filtering and sorting of orbital debris database entries using known spacecraft orbital parameters and maneuvering ∆V-budget to reduce the number of possible destinations for an optimizer to those which are in fact accessible from the spacecraft’s initial orbit. The chosen algorithm for analyzing and filtering the data is a two-burn node-to-node non-Hohmann transfer, which was used to estimate the ∆V-cost for transfer from the capture spacecraft’s initial orbit to an orbit near the target piece of debris. Once the ∆V-cost was calculated for each transfer orbit, entries with excessive fuel costs were removed from consideration, and the fuel cost to access each remaining orbit was appended to its entry. This method was capable of reducing a 10,400-item list of debris to less than 100 accessible targets in under 3 seconds on an ordinary laptop computer. This reduction in database size brought the number of targets down to a practical size for processing by a more computationally expensive optimization algorithm suitable for selecting final targets for a multi-capture spacecraft.