24 October 2017 A heuristic constraint programmed planner for deep space exploration problems
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Proceedings Volume 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation; 1046304 (2017) https://doi.org/10.1117/12.2281914
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
In recent years, the increasing numbers of scientific payloads and growing constraints on the probe have made constraint processing technology a hotspot in the deep space planning field. In the procedure of planning, the ordering of variables and values plays a vital role. This paper we present two heuristic ordering methods for variables and values. On this basis a graphplan-like constraint-programmed planner is proposed. In the planner we convert the traditional constraint satisfaction problem to a time-tagged form with different levels. Inspired by the most constrained first principle in constraint satisfaction problem (CSP), the variable heuristic is designed by the number of unassigned variables in the constraint and the value heuristic is designed by the completion degree of the support set. The simulation experiments show that the planner proposed is effective and its performance is competitive with other kind of planners.
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Xiao Jiang, Xiao Jiang, Rui Xu, Rui Xu, Pingyuan Cui, Pingyuan Cui, } "A heuristic constraint programmed planner for deep space exploration problems", Proc. SPIE 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation, 1046304 (24 October 2017); doi: 10.1117/12.2281914; https://doi.org/10.1117/12.2281914
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