Publisher's Note: This paper, originally published on 25 May 2023, was replaced with a corrected/revised version on 6 June 2023. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance.
In the field of machinery manufacturing, due to the backward detection methods of the outer dimensions of the workpiece, the high labor intensity of the detection and the high production cost, the problems of poor detection accuracy and low efficiency are caused. Therefore, this paper designs an industrial camera-based intelligent detection system for workpiece outer dimensions. The system uses industrial cameras to simulate human vision functions to accurately extract information from workpiece images and convert them into a form that can be understood and calculated by computers. Compared with the traditional manual workpiece external size detection method, the system has the advantages of good positioning flexibility, short sampling period, large amount of information, low cost, good stability, high precision and intelligence, etc., and the system realizes online non-contact detection, which has the characteristics of high accuracy, fast speed, and easy operation; at the same time, the system realizes the real-time measurement of the outer dimensions of the workpiece and the result output, and the detection accuracy is less than 0.01mm.Through the actual production environment test, the system can meet the requirements of rapid and accurate detection of the outer dimensions of the workpiece, and effectively enhance the ability of automatic manufacturing and detection of products.
Aiming at the problems that traditional ant colony algorithm is prone to fall into the local optimal solution and the convergence speed is slow in 3D path planning of UAVS, an improved ant colony algorithm is proposed for 3D path planning of UAVS. By improving the pheromone evaporation coefficient and combining with the evaluation function of A* algorithm, a three-dimensional environment model is constructed by using the grid method, and then experimental simulation is carried out. The results show that the convergence speed of the improved ant colony algorithm is faster and the optimal path is shorter than the traditional one.
ORB-SLAM2 based on depth camera cannot run directly in ROS operating system, and when the algorithm is implemented through PC, it does not efficiently carry computer resources, and reduces the flexibility of mobile robots. By building a ROS operating system in the embedded system, porting the ORB-SLAM2 algorithm in the system, optimizing the algorithm, adjusting the CPU usage of the algorithm operation, and using a mobile robot based on depth camera for mapping. The results show that the grid map generated by the ROS operating system can meet the needs of robot autonomous navigation, the SLAM effect is more intuitive, the system is more flexible, and the cost and hardware configuration requirements are greatly reduced. It is proved that the optimized algorithm is closer to the actual usage requirements.
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