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In a preparatory study conducted prior to the development of an active space debris removal system, a method for selecting target debris based on information such as the cumulative collision probability, the operational condition of objects, and their sizes and launch dates was developed for use in the protection of four Korea Multi-Purpose Satellite constellation satellites. This method can be used to select candidate removal targets. Two-line element data are used to identify threatening objects with high cumulative collision probability. Using information in the Satellite Catalog database, objects smaller than a certain size or objects that are currently operational were excluded from the selection range. The results of an analysis of the cumulative collision probability, object size, object type, and primary mission information showed that the COSMOS 1328, COSMOS 1862, COSMOS 375, and COSMOS 1606 satellites were suitable targets for an active debris removal mission. [ABSTRACT FROM AUTHOR]

In this paper, we analyze an evolutionary multiobjective heuristic algorithm for collision avoidance maneuver optimization when multiple objects in space threaten a satellite. Population-based evolutionary multiobjective heuristic algorithms can stably find optimal or suboptimal solutions on account of their robustness and flexibility. We present a collision avoidance maneuver optimization planning method using the nondominated sorting genetic algorithm II. Our proposed method simultaneously optimizes two goals: the minimization of DeltaV consumption and maximization of the satellite maneuver cycle length. Through an experimental evaluation, we demonstrate the efficient performance of our method in terms of DeltaV consumption and maneuver cycle length under constraints, including allowable DeltaV and burn start time, with consideration of a safety threshold between the user satellite and multiple threatening objects. We perform the simulation once per each burn strategy, i.e. in an in-track direction with a single burn; in three radial, in-track, cross-track direction with a single burn; and three radial, in-track, cross-track directions with two burns. We obtain various solutions, all of which satisfy the safety threshold. For the in-track direction with the single burn strategy, the solution showed merit in the minimization of DeltaV consumption. On the other hand, the results of three radial, in-track, cross-track directions with two burns showed good performance in maximization of the maneuver cycle. We therefore contend that efficient collision avoidance maneuver optimization planning can be achieved by using a multiobjective heuristic algorithm. [ABSTRACT FROM AUTHOR]

This paper compares and analyzes four heuristic algorithms for collision avoidance maneuver optimization (CAMO) when multiple space objects threaten a satellite. Classical gradient-based optimization methods are not appropriate for this kind of problem due to their discontinuities. On the other hand, heuristic algorithms can obtain suboptimal solutions due to their robustness and flexibility. In this paper, we develop CAMO planning methods using four heuristic algorithms. Their performance is compared in terms of the Del-V achieved under constraints on the minimum distance between the user satellite and multiple threatening objects, the maximum burn duration, and the boundary conditions for the maneuver start time. To validate the proposed strategy with the heuristic algorithms, two CAMO problems are analyzed. One is a simple problem using two control parameters (the maneuver start time and Del-V along the in-track direction) when a single threatening object is approaching. The second is a more complex CAMO problem that uses four control parameters (the maneuver start time and Del-V in three directions, i.e. radial, in-track, and cross-track) when four threatening objects are approaching from different angles and at different times. As a result, we minimize Del-V for each CAMO problem while satisfying all constraints. The differential evolution heuristic algorithm is found to exhibit the best performance in terms of minimized Del-V. [ABSTRACT FROM PUBLISHER]