Auto spray painting robots can realize the automation of various types of auto lamp spraying. The auto lamp detection algorithm is the key technology of the robot. The Faster R-CNN model is selected to detect the lamp, and some improvements are made to the model to achieve better detection effect. To generate more multi-dimensional feature expressions for auto lamps, residual network ResNet101 and feature pyra-mid network (FPN) are used to extract the features of auto lamps. Compatible Intersection over Union (CIoU) is used as the bounding box regression loss function to provide moving direction and more accurate position information for the bounding box and accelerate the convergence speed of the model. Through the auto lamp detection experiment, the average recognition accuracy of the improved model is 98.96%, and the detection speed is 0.46s per image. The improved model can realize the effective recognition of auto lamp types and meet the requirements of real-time processing.

As an important tool to explore and develop marine resources, underwater robot manipulator system is widely used in offshore oil engineering, underwater fishing and other industries at home and abroad. But in the complex underwater environment, the anti-interference ability of uvms(Underwater Vehicle Manipulator System) system has always been the focus of current research. This paper aims to start with the commonly used PID control method, design and simulate the active disturbance rejection controller, and compare with the traditional control method. Finally, experiments are carried out on the uvms system in pool and field lake. The experimental results show that the uvms system has stable motion and good maneuverability, and the underwater manipulator can grasp the underwater target quickly and accurately.

In this paper, the optimization method of lamp spraying trajectory is studied, the parabolic coating thickness accumulation rate model is established, and a segmentation algorithm based on lamp spraying is proposed. Taking the deviation between the thickness of the overlapping area and the ideal thickness as the optimization objective, the width and the spraying speed of the two adjacent spraying paths are determined. An improved particle swarm optimization (PSO) algorithm based on edge recombination operator is proposed for the optimal combination of all spraying trajectories on the lamp surface. Simulation experiments and spraying experiments verify the feasibility of the algorithm.

Real-time detection of apples in natural environment is a necessary condition for robots to pick apples automatically, and it is also a key technique for orchard yield prediction and fine management. To make the harvesting robots detect apples quickly and accurately in complex environment, a Des-YOLO v4 algorithm and a detection method of apples are proposed. Compared with the current mainstream detection algorithms, YOLO v4 has better detection performance. However, the complex network structure of YOLO v4 will reduce the picking efficiency of the robot. Therefore, a Des-YOLO structure is proposed, which reduces network parameters and improves the detection speed of the algorithm. In the training phase, the imbalance of positive and negative samples will cause false detection of apples. To solve the above problem, a class loss function based on AP-Loss (Average Precision Loss) is proposed to improve the accuracy of apple recognition. Traditional YOLO algorithm uses NMS (Nonmaximum Suppression) method to filter the prediction boxes, but NMS cannot detect the adjacent apples when they overlap each other. Therefore, Soft-NMS is used instead of NMS to solve the problem of missing detection, so as to improve the generalization of the algorithm. The proposed algorithm is tested on the self-made apple image data set. The results show that Des-YOLO v4 network has ideal features with a mAP (mean Average Precision) of apple detection of 97.13%, a recall rate of 90%, and a detection speed of 51 f/s. Compared with traditional network models such as YOLO v4 and Faster R-CNN, the Des-YOLO v4 can meet the accuracy and speed requirements of apple detection at the same time. Finally, the self-designed apple-harvesting robot is used to carry out the harvesting experiment. The experiment shows that the harvesting time is 8.7 seconds and the successful harvesting rate of the robot is 92.9%. Therefore, the proposed apple detection method has the advantages of higher recognition accuracy and faster recognition speed. It can provide new solutions for apple-harvesting robots and new ideas for smart agriculture.