With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called "basic-confrontation", which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.

UAV trajectory prediction is the core technology for autonomous UAV flight and is a prerequisite for control and navigation. In this paper, the UAV flight path prediction model is established by collecting the flight data of the actual UAV. Firstly, the UAV flight information collection and data preprocessing are carried out; Secondly, the UAV flight state recognition model is established based on the PCA-SVM model to identify five UAV flight states; Finally, the flight path prediction model of UAV based on flight state recognition is established, and the neural network model is established based on the flight path of five flight state recognition. The experimental results show that: 1) The accuracy of UAV flight state recognition based on PCA-SVM is more than 90%. 2) The average prediction error of the traditional neural network UAV trajectory is 0.422m, and the maximum error of the circling state is 0.84m. 3) The average prediction error of the UAV flight path based on flight state recognition is 0.214m, and the maximum error of the circling state is 0.41m. The model error is less than 0.5m. The results show that the prediction model with flight state recognition has significantly less error than the direct UAV trajectory prediction, and the prediction model with flight state recognition predicts better than the traditional Unscented Kalman Filter method.

The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.

With the rapid development of electronic science and technology, the research on wearable devices is constantly updated. Still, the current wearable devices are not yet comprehensive in recognizing and analyzing the movement patterns of special sports. Based on this, this paper improves wearable devices for table tennis, a single sport, and realizes the recognition and evaluation of table tennis players’ motor skills based on the pattern recognition method and hierarchical evaluation system. First, a table tennis player’s motion information acquisition device was designed and the collected motion data were processed. Secondly, a sliding window was designed to segment the collected motion data and establish a feature database of six table tennis benchmark movements. Secondly, the motion features were constructed based on feature engineering, and after dimensionality reduction, the different models were used for action skill recognition. Finally, a hierarchical evaluation system of movement skills was established and the loss functions of different evaluation indexes were given. The experimental results show that in table tennis players’ action skill recognition, the neural network model based on motor feature extraction proposed in this paper improves the recognition accuracy by 6.5% than the traditional convolutional neural network, and in table tennis players’ action skill evaluation, the ANOVA shows that the hierarchical evaluation system proposed in this paper has a significance level of p<0.05 for the athletes’ professionalism level. The conclusion of the study indicates that the proposed paper table tennis players’ motor skills recognition and hierarchical evaluation assessment system can realize the accurate recognition of athletes’ motor skills and the precise determination of their professional level.

With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.