Task scheduling and attitude planning for agile earth observation satellite with intensive tasks

This paper investigates the task scheduling and attitude planning of single agile earth observation satellite for intensive tasks. We aim to obtain the execution strategy and corresponding attitude path at the same time by the maximization of the total number of imaged tasks together with minimization of the energy cost, where the rectangle strip observation tasks are considered. Denoting the ground task as a time-varying attitude path, the whole collaborative task scheduling and attitude planning problem is modeled in the attitude apace. To prepare for the final solution, we first design a time-dependent mechanism to determinate the start working time, based on which an energy-dependent mechanism is designed to determine the attitude path corresponding for each possible execution strategy. Using the results produced by the mechanisms to construct the evaluation process, we propose a novel pseudospectral cooperative genetic algorithm (PCGA) to obtain the effective execution strategy and attitude path simultaneously. In PCGA, the genetic algorithm is employed to search the optimal solution, while the pseudospectral procedure and cooperative concept are combined into the genetic algorithm framework to further eliminate the uncertainty and obtain a compromise solution between two objectives, respectively. Simulation results demonstrate the effectiveness of proposed model and algorithm.