Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima. Additionally, it enhances the path smoothness, which improves the path optimisation. This enhances the capacity of UAVs to optimise their paths in environments characterised by multiple obstacles. To evaluate the practical effectiveness of the algorithm, a three-dimensional complex city model was constructed for the purposes of the study, and an adaptation function was designed for the purpose of evaluation. The experimental evaluation of 23 benchmark functions, the simulation test of the 3D city model, and 100 repetitive experiments demonstrate that the composite improved algorithm has a considerable advantage over the other comparative algorithms regarding performance. It exhibits fast convergence, high accuracy, and both global and local search capabilities, which enable the effective planning of a UAV flight path and the maintenance of good stability. In comparison to traditional algorithms, the composite improved algorithm demonstrably reduces the flight time and the number of obstacle avoidance manoeuvres required by the UAV. It provides robust technical support for the path planning of the UAV in complex urban environments and facilitates the advancement and implementation of related technologies.

The article establishes a seven-degree-of-freedom projectile trajectory model for a new type of spinning projectile. Based on this model, a numerical analysis is performed on the ballistic characteristics of the projectile, and the trajectory of the dual-spinning projectile is filtered with the unscented Kalman filter algorithm, so that the measurement information of projectile onboard equipment is more accurate and more reliable measurement data are provided for the guidance system. The numerical simulation indicates that the dual-spinning projectile is mainly different from the traditional spinning projectile in that a degree of freedom is added in the direction of the axis of the projectile, the forebody of the projectile spins at a low speed or even holds still to improve the control precision of the projectile control system, while the afterbody spins at a high speed maintaining the gyroscopic stability of the projectile. The trajectory filtering performed according to the unscented Kalman filter algorithm can improve the accuracy of measurement data and eliminate the measurement error effectively, so as to obtain more accurate and reliable measurement data.

The purpose of this paper is to present an in‐flight initial alignment method for the guided projectiles, obtained after launching, and utilizing the characteristic of the inertial device of a strapdown inertial navigation system. This method uses an Elman neural network algorithm, optimized by genetic algorithm in the initial alignment calculation. The algorithm is discussed in details and applied to the initial alignment process of the proposed guided projectile. Simulation results show the advantages of the optimized Elman neural network algorithm for the initial alignment problem of the strapdown inertial navigation system. It can not only obtain the same high‐precision alignment as the traditional Kalman filter but also improve the real‐time performance of the system.

Improvement in attack accuracy of the spin projectiles is a very significant objective, which increases the overall combat efficiency of projectiles. The accurate determination of the projectile roll attitude is the recent objective of the efficient guidance and control. The roll measurement system for the spin projectile is commonly based on the magnetoresistive sensor. It is well known that the magnetoresistive sensor produces a sinusoidally oscillating signal whose frequency slowly decays with time, besides the possibility of blind spot. On the other hand, absolute sensors such as GPS have fixed errors even though the update rates are generally low. To earn the benefit while eliminating weaknesses from both types of sensors, a mathematical model using filtering technique can be designed to integrate the magnetoresistive sensor and GPS measurements. In this paper, a mathematical model is developed to integrate the magnetoresistive sensor and GPS measurements in order to get an accurate prediction of projectile roll attitude in a real flight time. The proposed model is verified using numerical simulations, which illustrated that the accuracy of the roll attitude measurement is improved.

This article details a new optimizing algorithm called Adaptive Chaotic Mutation Particle Swarm Optimization (ACM-PSO). The new algorithm is used to perform aerodynamic parameter estimation on a spinning symmetric projectile. The main creative ideas of this new algorithm are as follows. First, a self-adaptive weight function is used so that the inertial weight can be adjusted dynamically by itself. Second, the initialized particle is generated by chaos theory. Last, a method that can be used to judge whether the algorithm has fallen into a local optimum is established. The common testing function is used to test the new algorithm, and the result shows that, compared with the basic particle swarm optimization (PSO) algorithm, it is more likely to have a quick convergence and high accuracy and precision, leading to extensive application. Simulated ballistic data are used as testing data, and the data are subjected to the new algorithm to identify the aerodynamic parameters of a spinning symmetric projectile. The result shows that the algorithm proposed in this paper can effectively identify the aerodynamic parameters with high precision and a quick convergence velocity and is therefore suitable for use in actual engineering.

This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.