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Proceedings Paper

A heuristic constraint programmed planner for deep space exploration problems
Author(s): Xiao Jiang; Rui Xu; Pingyuan Cui
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Paper 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.

Paper Details

Date Published: 24 October 2017
PDF: 6 pages
Proc. SPIE 10463, AOPC 2017: Space Optics and Earth Imaging and Space Navigation, 1046304 (24 October 2017); doi: 10.1117/12.2281914
Show Author Affiliations
Xiao Jiang, Beijing Institute of Technology (China)
Rui Xu, Beijing Institute of Technology (China)
Pingyuan Cui, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10463:
AOPC 2017: Space Optics and Earth Imaging and Space Navigation
Carl Nardell; Suijian Xue; Huaidong Yang, Editor(s)

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