As combat missions become increasingly complex in both space and time, cross-regional joint operations (CRJO) is becoming an overwhelming trend in modern air warfare. How to allocate resources and missions prior to the operation becomes a central issue to improve the combat efficiency. In this paper, we focus on the cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs) in a CRJO. A multi-objective optimization problem is presented with the aim of minimizing the makespan while maximizing the value expectation obtained. Moreover, it is not mandatory for each UAV to return exactly to the base which it takes off. Furthermore, in addition to the constraints commonly found in UAV mission assignment problems, the ammunition inventory at each base is also taken into account. To solve such a problem, we developed an improved genetic algorithm (IGA) with a novel chromosome encoding format. It can determine the number of attacks on a given target based on the expectations obtained, rather than being predetermined. Specifically, an efficient logic-based unlocking mechanism is designed for the crossover and mutation operations in the algorithm. Simulation results show that the developed IGA can efficiently solve the considered problem. Through numerical experimental comparisons, the algorithm proposed in this work is superior to other existing IGA-like algorithms in terms of computational efficiency.

With the increasing complexity of modern air warfare, an efficient and robust mission planning, which mainly includes task assignment and path planning, becomes the key issue to improve the combat efficiency. This paper reviews recent progress in UAV mission planning. First, basic concepts of UAVs and their mission planning problem are given. And several representative existing mission planning systems are briefly introduced. The constraints and objectives in the task assignment model are reviewed, and the pros and cons of algorithms commonly used are then summarized. After that, the algorithms for path planning are reviewed. Finally, we point out current problems and future research directions. The paper provides a comprehensive review of the field and enables a quick start for those who aim to do related research.