UAV mission planning is one of the core problems in the field of UAV applications. Currently, mission planning needs to simultaneously optimize multiple conflicting objectives and take into account multiple mutually coupled constraints, and traditional optimization algorithms struggle to effectively address these difficulties. Constrained multi-objective evolutionary algorithms have been proven to be effective methods for solving complex constrained multi-objective optimization problems and have been gradually applied to UAV mission planning. However, recent advances in this area have not been summarized. Therefore, this paper provides a comprehensive overview of this topic, first introducing the basic classification of UAV mission planning and its applications in different fields, proposing a new classification method based on the priorities of objectives and constraints, and describing the constraints of UAV mission planning from the perspectives of mathematical models and planning algorithms. Then, the importance of constraint handling techniques in UAV mission planning and their advantages and disadvantages are analyzed in detail, and the methods for determining individual settings in multiple populations and improvement strategies in constraint evolution algorithms are discussed. Finally, the method from the related literature is presented to compare in detail the application weights of constrained multi-objective evolutionary algorithms in UAV mission planning and provide directions and references for future research.

Multi-UAV cooperative trajectory planning (MUCTP) refers to the planning of multiple flyable trajectories based on the location of each UAV and mission point in a complex environment. In the planning process, the complex 3D space structure increases the difficulty of solving the trajectory points, and the mutual constraints of the UAV cooperative constraints can degrade the performance of the planning system. Therefore, to improve the efficiency of MUCTP, this study proposes MUCTP based on feasible domain space and adaptive differential evolution algorithm (FDS-ADEA). The method first constructs a three-dimensional feasible domain space to reduce the complexity of the search space structure; then, the constraints of heterogeneous UAVs are linearly weighted and transformed into a new objective function, and the information of the fitness value is shared in accordance with the adaptive method and the code correction method to improve the search efficiency of the algorithm; finally, the trajectories are smoothed to ensure the flyability of the UAVs performing the mission by combining the cubic B-spline curves. Experiments 1, 2, 3, and 4 validate the proposed algorithm. Simulation results verify that FDS-ADEA has fast convergence, high cooperative capability, and more reasonable planned trajectory sets when processing MUCTP.

Multi-satellite imaging mission planning has been difficult in various scenarios due to the complex constraints of multi-satellite imaging, the wide area covered by target points, and the difficulty of achieving different mission requirements in a short period of time with limited satellite resources. In addressing this challenge, this work investigates multi-satellite imaging mission planning based on the Unified Plan Model and Improved Adaptive Differential Evolution algorithm (UPM-IADE). First, a unified model is built based on two scenarios: a large-scale imaging mission and an emergency support mission, and then a mission assignment framework is adaptively selected based on mission priority. Second, a monorail task synthesis method based on visible time windows is created to clarify the execution relationship between the satellite and the target point. Finally, an individual weight ranking rule is developed, and the weight is used to combine the fitness value ranking and diversity ranking into a final fitness value ranking, which is used to select individuals that satisfy the mutation requirements into the mutation strategy pool for adaptive mutation strategy selection. Experiments 1, 2, 3, and 4 have demonstrated that UPM-IADE can successfully resolve the imaging satellite mission planning for both scenarios while providing remarkable performance in terms of high mission benefit and rapid response.

With the increasing complexity of unmanned aerial vehicle (UAV) missions, single-objective optimization for UAV trajectory planning proves inadequate in handling multiple conflicting objectives. There is a notable absence of research on multi-objective optimization for UAV trajectory planning. This study introduces a novel two-stage co-evolutionary multi-objective evolutionary algorithm for UAV trajectory planning (TSCEA). Firstly, two primary optimization objectives were defined: minimizing total UAV flight distance and obstacle threats. Five constraints were defined: safe distances between UAV trajectory and obstacles, maximum flight altitude, speed, flight slope, and flight corner limitations. In order to effectively cope with UAV constraints on object space limitations, the evolution of the TSCEA algorithm is divided into an exploration phase and an exploitation phase. The exploration phase employs a two-population strategy where the main population ignores UAV constraints while an auxiliary population treats them as an additional objective. This approach enhances the algorithm’s ability to explore constrained solutions. In contrast, the exploitation phase aims to converge towards the Pareto frontier by leveraging effective population information, resulting in multiple sets of key UAV trajectory points. Three experimental scenarios were designed to validate the effectiveness of TSCEA. Results demonstrate that the proposed algorithm not only successfully navigates UAVs around obstacles but also generates multiple sets of Pareto-optimal solutions that are well-distributed across objectives. Therefore, compared to single-objective optimization, TSCEA integrates the UAV mathematical model comprehensively and delivers multiple high-quality, non-dominated trajectory planning solutions.

Multi-satellite imaging mission planning (MSIMP) research has advanced substantially in recent years. However, contemporary MSIMP research in uncertain environments is still confronting challenges such as loss of satellite resource allocation, inadequate anti-jamming ability of the mission planning scheme, and low mission completion rate. Therefore, in this work, we propose a hybrid local replanning strategy improved adaptive differential evolutionary (HLRS-MSFADE) algorithm based on the multi-satellite imaging mission planning in uncertain environments (MSIMPUE). First, an MSIMPUE model based on uncertainty assessment is constructed. This model solves the problem of assessing new tasks with varied qualities to decide the observation order in an uncertain environment and decreases the loss caused by inefficient satellite resource allocation. Second, to address the issue of difficulty in planning for changing new task requirements in uncertain environments, an HLRS for uncertain environments is developed to ensure efficient task insertion while avoiding conflict costs. Finally, an MSFADE algorithm is presented to handle the problem of long MSIMPUE mission response time and low mission completion rate with good quality in an acceptable computation time. The simulation results validated the effectiveness and stability of the method in dealing with MSIMPUE. Moreover, the HLRS-MSFADE algorithm outperforms previous methods in terms of mission response time, mission completion rate, and motion perturbation.