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.

The deployment of megaconstellations in low Earth orbit (LEO) poses significant collision risks with space debris. This paper focuses on analyzing the short-term and long-term collision probabilities of megaconstellations to assess their collision risk. Firstly, a short-term collision risk evolution model is developed to accurately address rendezvous collisions. Secondly, a long-term collision risk evolution model is established by considering space object density, space debris attenuation, space target disintegration, and the distribution of disintegration targets. Through simulations conducted on the Starlink Phase I constellation, the results demonstrate a 30–40% increase in short-term collision probability within the constellation shell, a 70.2% probability of at least one collision during the constellation’s lifetime, and a 25.3% increase in secondary collisions following a collision event. This study provides a reference and application for analyzing the orbital safety of LEO megaconstellations and for promoting the sustainable development and utilization of space resources.

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.

A constellation configuration performance evaluation method is proposed for the performance evaluation of the low-orbit large-scale communication satellite constellations. The practicality and feasibility analysis of the constellation configuration is mainly studied from the constellation coverage performance. Based on the consideration of the coverage performance of the LEO satellite constellation, four simulation models are established for the single coverage rate, observation elevation angle, number of visible satellites under different observation elevation angles, and coverage efficiency of the constellation. A population distribution density function is established according to the characteristics of population distribution to find the average minimum observation elevation angle and the average number of visible satellites under the population distribution. The evaluation method is applied to three typical low-orbit large-scale communication satellite constellations, Telesat, OneWeb, and Starlink, to derive the coverage performance index values of each constellation and to compare and analyze the characteristics of the three constellations. The results show that the evaluation method can evaluate the configuration performance of different types of LEO large-scale constellations and provide a basis and reference for the optimal design and evaluation of future LEO large-scale constellation configurations.

With the increase in satellites in the medium Earth orbit (MEO) region, there should be a focus on orbit safety in the MEO region. A safe orbit disposal strategy is necessary to maintain the sustainability of the MEO region. This paper focuses on long-term evolution modeling, safety analysis of MEO objects, and different disposal techniques for end-of-life BDS-2 MEO satellites. On the one hand, a long-term numerical evolution model is established, and mean equinoctial elements are adopted to propagate a long-term orbit. Long-term evolution for the MEO region over 100 years is carried out, including the Galileo, BDS, GPS, and GLONASS constellations. The earliest orbit intersection time with other global navigation satellite system (GNSS) constellations is put forward. On the other hand, a dynamic model and an optimization model for disposal orbit are established, which minimize the eccentricity growth within 200 years and the fuel consumption for maneuvering to the disposal orbit. The bounds for the disposal region of BDS MEO satellites are also proposed, which consider the measurement and control error of BDS MEO satellites and the eccentricity bounds for end-of-life BDS MEO satellites. A genetic algorithm is adopted to optimize the orbital elements for end-of-life BDS MEO satellites. In addition, two disposal cases, namely, upraising and reducing the orbit, for end-of-life BDS MEO satellites are simulated. The long-term evolutions for the disposal of orbital elements within 200 years are implemented, and the fuel consumption is calculated. The results show that the current MEO region is relatively safe and that the eccentricity is the most important factor that influences the long-term evolution of safety analysis for BDS MEO disposal orbits. Upraising the orbit is safe for end-of-life BDS MEO satellites. This investigation provides the theoretical foundation for investigating the long-term evolutionary mechanisms of the MEO region and references disposal strategy analysis for decommissioned navigation satellites, and the spent upper stages for other GNSS constellations.