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.

Low Earth orbit (LEO) satellites have unique advantages in navigation because of their high signal intensity and rapid geometric changes in a short period. In order to solve the problem of constellation performance degradation after a potential failure pertaining one or more medium Earth orbit (MEO) navigation satellites, this paper designs the LEO navigation constellation and considers the task requirements of different stages of constellation deployment. Firstly, the LEO navigation constellation is designed by a non-dominated sorting genetic algorithm II (NSGA-II). The average position dilution of precision (PDOP) is 1.676, which is an improvement compared to the average PDOP offered by the four traditional GNSS. Secondly, the staged deployment of constellation takes into account the degradation of constellation performance caused by the failure of MEO navigation satellites, and the Monte Carlo method is used to analyze the case of three simultaneous satellite failures. The results show that a single satellite failure within each orbital plane and adjacent satellites with close phase separation has a great impact on the performance of the MEO navigation constellation. On this basis, a staged deployment strategy was adopted in order to balance cost, risk, and performance. The three phases deploy 66, 156, and 288 satellites, respectively; as a make-up constellation under contingencies, a navigation enhancement constellation, and an independent navigation constellation, the deployment of the staged sub-constellations meets the mission requirements. The constellation design and staged deployment method proposed in this paper can provide reference for the future study of LEO navigation constellations.

This paper investigates the fly-around formation between the servicer spacecraft and the target spacecraft. Inspired by the spacecraft orbital motion under the Earth’s gravity, an intuitive, analytical guidance law for spatial fly-around formation design with the low-thrust maneuver is proposed. Beginning with the relative translational dynamics based on relative position and velocity, the control input of the guidance law is designed to contain two parts. The first part is the feed-forward term, which makes the relative dynamics a second-order integration model. The second part is the artificial gravity term, which has similar expressions to the Earth’s gravity, and includes the artificial gravitational coefficient and the vector of the artificial gravity center. The above two parameters can be designed to determine the size, shape, and period of the fly-around trajectory. Specifically, three kinds of fly-around trajectories are discussed in detail. The first two are the spatial ellipses with the target spacecraft locating at the focus and the center of the ellipses, respectively. The third is the spatial circle. The proposed method can be easily extended to the design of planar fly-around formation, which is very systematic and comprehensive, and the fuel consumption of the control input is specifically discussed. Numerical simulations are conducted to demonstrate the efficiency of the proposed method.

This paper proposes a pose control scheme of for proximity maneuvering for tracking and observing noncooperative targets with unknown acceleration, which is an important prerequisite for on-orbit operations in space. It mainly consists of a finite-time extended state observer and constraint processing procedures. Firstly, relative pose-coupled kinematics and dynamics models with unknown integrated disturbances are established based on dual quaternion representations. Then, a finite-time extended state observer is designed using the super-twisting algorithm to estimate the integrated disturbances. Both observation field of view and collision avoidance pose-constrained models are constructed to ensure that the service spacecraft continuously and safely observes the target during proximity maneuvering. And the constraint models are further incorporated into the design of artificial potential function with a unique minimum. After that, the proportional–derivative-like pose-constrained tracking control law is proposed based on the estimated disturbances and the gradient of the artificial potential function. Finally, the effectiveness of the control scheme is verified through numerical simulations.