Abstract
This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.
Highlights
Earth observation satellites are important for scientific research, military reconnaissance, agricultural harvesting, and so on
The results show that the optimization effect of the genetic algorithm (GA) algorithm is better than the particle swarm optimization (PSO) algorithm in a limited planning time
The improved clique partitioning algorithm was applied to obtain the clustering tasks, which were the input for the task planning
Summary
Earth observation satellites are important for scientific research, military reconnaissance, agricultural harvesting, and so on. Globus et al [9] compared the performance of designed a multi-objective genetic algorithm for fast response to natural disasters [15] He R and Bai B various algorithms, such as the simulated annealing algorithm, hill climbing algorithm, and genetic established the mission planning model by considering the synthetic observation between missions; to algorithm, for the imaging scheduling problem of point targets. Due to the above two cases, this paper adopts a genetic algorithm–annealing algorithm (GA-SA) hybrid optimization algorithm, aiming to maximize the priority and the number of completions and considering the mission constraints including the satellite visible window, the sensor observation without overlapping, the preparation time for the sensor operation, slew angle, the observation time of the task, the single longest boot duration, the single maximum boot time, satellite energy, and data storage. The experimental simulation results show that the improved clustering algorithm combined with the GA-SA hybrid planning algorithm have a higher observation performance, improving the mission execution efficiency
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