Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera.

Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.

With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.

Whether it is for military or civilian use, quadrotor UAV has always been one of research central issues. Most of the current quadrotor drones are manually operated and use GPS signals for navigation, which not only limits the operating range of the drone but also consumes a lot of manpower and material resources. This research mainly studies the method of realizing autonomous flight and conflict avoidance of quadrotor UAV by using multisensor system and deep learning method in extreme flight conditions through track prediction. The convolutional neural network method is used to extract the image information collected by the UAV sensor system. And it uses the cyclic neural network to extract the time feature of the information collected by the UAV sensor. The research results show that the track prediction method based on the deep learning method has higher flight accuracy for quadrotor UAVs. The yaw error of the spatial position is only 2.82%, and the maximum error of the time characteristic error tolerance is only 0.77%.

In order to solve the problems of low efficiency of power inspection, large safety hazards, and mainly relying on manual labor, we proposed a vertical take-off and landing fixed-wing UAV with AI algorithm. The drone in this project is equipped with the NVIDIA AGX Orin edge computing module, which has a computing power of up to 275TOPS. In addition, it incorporates artificial intelligence algorithms such as wildfire recognition, human and animal identification, and target localization. These algorithms enable drones to detect targets such as wildfires and foreign object intrusions while performing power inspection tasks, and locate and track these targets at the same time. Finally, the picture is transmitted back to the terminal through the 5G module and handed over to manual processing. Overall, this approach can significantly improve the efficiency and safety of the power testing process.

To address challenges such as small target sizes, blurred target features, and difficulty in distinguishing between targets and backgrounds in small object detection, we propose a method based on Multi-Scale Image Degradation combined with the Contrastive Learning model. By leveraging contrastive learning techniques, our approach aims to enhance the discriminative features necessary for accurately distinguishing objects from backgrounds. To specifically target small objects, we subject target samples to various multi-scale image degradation modes before inputting them into the contrastive learning model. Augmentation techniques are then applied to these degraded samples to facilitate effective contrastive feature learning. Consequently, the model is better equipped to uncover the differences between small targets and backgrounds, thereby improving small object detection performance. Furthermore, considering that spatial domain features are sensitive to local changes in the image, while frequency domain features are sensitive to global structural changes, our approach applies the contrastive learning model in both spatial and frequency domains, aiming to acquire more robust features for small object detection. Extensive experiments conducted on the MS COCO dataset and the VisDrone2019 dataset validate the effectiveness of our proposed method in significantly enhancing small object detection accuracy.