A Spacecraft Onboard Autonomous Task Scheduling Method Based on Hierarchical Task Network-Timeline

Abstract To address the inherent challenges of deep space exploration, such as communication delays and the unpredictability of spacecraft environments, this study focuses on enhancing spacecraft adaptability and autonomy, which are essential for Autonomous Space Scientific Exploration. A pivotal aspect of this endeavor is the advancement of spacecraft task scheduling, which is integral to increasing spacecraft autonomy. Current research in this domain predominantly revolves around mission timing planning and is primarily executed from ground stations. However, these plans often lack the granularity required for direct implementation by spacecraft. In response, our study proposes an innovative approach to augment spacecraft autonomy, introducing a method that articulately describes mission objectives and resource information. We designed a novel hierarchical task network-timeline (HTN-T) algorithm, an amalgamation of the HTN scheduling method and the distinctive elements of existing research. This algorithm addresses time constraints through horizontal and vertical expansions, building upon the resolution of logical constraints found in conventional planning methods. Furthermore, it introduces a priority-based strategy for resolving resource conflicts in spacecraft tasks. This algorithm is substantiated through validation, including proof-of-principle demonstrations and assessments within a Space–ground Collaborative Management and Control System encompassing both ground and spacecraft operations. The findings indicate that our proposed algorithm achieves high rates of scheduling success and operational efficiency within a feasible timeframe, thus effectively navigating the complexities of autonomous spacecraft task scheduling.

Download paper here

Recommended citation: [1]Zhang J, Lyu L. A Spacecraft Onboard Autonomous Task Scheduling Method Based on Hierarchical Task Network-Timeline. [J]Aerospace. 2024; 11(5):350. https://doi.org/10.3390/aerospace11050350.