Multi-objective Optimization of Attitude Maneuver Planning for Flexible Asteroid Lander Using Population Evolutionary Algorithm

In order to solve the problem of attitude maneuvering control and attitude planning in asteroid flexible attachment scenario with multiple performance index parameters optimized, in this paper, an approach to multi-objective optimization of attitude maneuver path planning for asteroid-attached flexible lander is proposed. By constructing a three-node coupling dynamic model, the attitude description and dynamic constraint characterization of the flexible three-node probe are realized. For multi-objective optimization of maneuver time and flexible deformation, the attitude objective function is designed to serve as the population selection criterion. The RRT-based path planning method is adopted to generate pointing constraint-compliant initial path with local optimized expansion strategy. Whereafter, a population evolutionary algorithm on the basis of the fruit fly optimization algorithm with non-dominated sorting solution is designed to include the initial path in smell concentration searching with a certain probability, so as to improve the convergence rate and program operation efficiency, and realize the multi-objective attitude maneuver under pointing constraint. Finally, the feasibility and effectiveness of this method is verified by numerical simulation of large angle attitude maneuver of three-node flexible probe.