An algorithm of dynamic temporal constraints for the mission series in deep space detectors

Abstract

Deep space exploration activities can effectively promote the development and application of space technology, which holds significant scientific and strategic value. The multi-mission and long-period detector is executed in deep space, and numerous temporal constraints are generated by mission planning. Moreover, scheduled missions may face external uncertainties, hindering their smooth execution. Therefore, temporal constraint processing strategies that possess dynamic adjustment capabilities play a crucial role in ensuring the successful execution of deep space detectors. Existing methods primarily concentrate on investigating fixed mission series, with limited efficiency in planning. Specifically, these methods do not rigorously enforce temporal constraints and lack the capability to handle the disruptions caused by emergencies, which hinder the smooth execution of planned missions. In this paper, we propose a dynamic temporal constraints algorithm, aiming to efficiently plan missions subject to temporal constraints and let it possess a certain degree of dynamic adjust capability. The simulation results demonstrate the effective simplification of redundant temporal constraints in mission networks by the proposed algorithm. In comparison to the classical algorithm, the computation time exhibits a clear advantage. Furthermore, the proposed algorithm possesses the capability to perform local, small-scale dynamic adjustments in response to emergencies that may disrupt the normal execution of missions. Consequently, this research establishes a robust basis for addressing temporal constraints in deep space detectors.