Multisatellite cooperative task planning method based on tabu-genetic feedback optimization

Abstract

Given the exponential growth in satellite numbers and mission demands, the imperative to sufficiently harness satellite observation capabilities has propelled multi-satellite cooperative observation into an inevitable trajectory. Task planning of multi-satellite is a typical NP-hard problem characterized by an extensive solution scale, which is conventionally addressed using a single heuristic algorithm. However, the employment of such algorithms is con-strained by limitations like premature convergence, thereby presenting challenges in tackling this problem. To overcome this issue, this paper initially establishes a parameter optimization problem for coordinated scheduling of multi-satellites with maximizing observation profit as the performance index. Secondly, a two-layer solution architecture is proposed based on Tabu Search and Genetic Algorithm (TS-GA) feedback optimization to decompose the original problem into subproblems of mission allocation of multi-satellite and several single-satellite mission scheduling. The simulation results demonstrate that TS-GA facilitates acquiring a superior observation scheme. Owing to the implementation of a load-balanced strategy, TS-GA exhibits faster convergence and achieves a more reasonable distribution of planning results.