Aiming at the problems of strong nonlinearity, strong coupling and unknown interference encountered in the flight control process of unmanned aerial vehicles (UAVs) in a complex dynamic environment and reinforcement learning-based algorithm generalization, this study presents an innovative incremental reinforcement learning-based algorithm for UAV tracking control in a dynamic environment. The main goal is to make a UAV able to adjust its control policy in a dynamic environment. The UAV tracking control task is transformed into a Markov decision process and further investigated using an incremental reinforcement learning-based method. First, a policy relief method is employed to make UAVs capable of performing an appropriate exploration in a new environment. In this way, a UAV controller can mitigate the conflict between a new environment and the current knowledge to ensure better adaptability to a dynamic environment. In addition, a significance weighting method is developed to improve the utilization of episodes with higher importance and richer information. In the proposed method, learning episodes that include more useful information are assigned with higher importance weights. The numerical simulation, hardware-in-the-loop experiments, and real-world flight experiments are conducted to evaluate the performance of the proposed method. The results demonstrate high accuracy and effectiveness and good robustness of the proposed control algorithm in a dynamic flight environment.

This study presents an innovative reinforcement-learning-based control algorithm for a vertical take-off and landing (VTOL) aircraft under wind disturbances. In our approach, the tracking control problem of the VTOL aircraft is formulated as a Markov decision process, and the appropriate system state, reward function, and soft update method are presented. To improve the control accuracy under wind disturbances, three kinds of wind fields were added in the learning environment to expand the exploration space and simulate the effect of wind disturbances on the flight control. Moreover, to ensure the tracking accuracy and the practical implementation, a quantum-inspired experience replay strategy was developed based on quantum computation theory. In this strategy, the preparation operation scheme was designed to encourage the exploration and speed up the convergence. The depreciation operation method was developed to enrich the sample diversity, which increased the robustness of the controller and allowed the control strategy learned in the numerical simulations to be directly transferred into real-world environments. Numerical simulations, hardware-in-the-loop experiments, and real-world flight experiments were conducted to evaluate the performance and merits of the proposed method. The results demonstrated high accuracy and effectiveness and good robustness of the proposed control algorithm in terms of standoff target tracking control and flight stability.

When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.

To address the challenges of dynamic perception, real-time decision-making, and control stability in UAV visual tracking tasks, this study proposes an Attention-guided Visual Servoing Reinforcement Learning (AVSRL) framework. Unlike conventional Image-Based Visual Servoing (IBVS) methods that rely on analytical Jacobian control, AVSRL employs a virtual camera mechanism to normalize image observations and extract geometry-aware visual features as state inputs to a deep reinforcement learning (DRL) agent. The proposed framework integrates model identification, attention-enhanced actor-critic learning, and multi-source visual-environment encoding to enable robust and adaptive UAV control in dynamic and complex environments. A multi-head attention module selectively processes spatial-temporal observations from both static and dynamic objects, while a centralized critic facilitates cooperative optimization across agents. Extensive simulations and real-world experiments were conducted, including average reward analysis, hyperparameter sensitivity evaluation, trajectory tracking of complex geometric paths, and pedestrian following in outdoor scenarios. Results show that AVSRL outperforms baseline DRL and IBVS controllers in terms of tracking accuracy, control smoothness, and adaptability to visual and environmental variability. The findings validate the AVSRL framework as a promising solution for real-time UAV navigation and tracking tasks under uncertain and complex visual conditions.

The method of using unmanned aerial vehicle (UAV) to obtain aerial image information of target scene has the characteristics of wide coverage, strong mobility and high efficiency, which is widely used in urban traffic monitoring, vehicle detection, oil pipeline inspection, regional survey and other aspects. Aiming at the difficulties of the object to be detected in the process of aerial image object detection, such as multiple orientations, small image pixel size and UAV body vibration interference, a novel aerial image object detection model based on the rotation-invariant deep denoising auto encoder is proposed in this paper. Firstly, the interest region of the aerial image is extracted by the selective search method, and the radial gradient of interest region is calculated. Then, the rotation invariant feature descriptor is obtained from the radial gradient feature, and the noise in the original data is filtered out by the deep denoising automatic encoder and the deep feature of the feature descriptors is extracted. Finally, the experimental results show that this method can achieve high accuracy for aerial image target detection and has good rotation invariance.