The structure, function and working principle of JLUIV-3, which is a new typeof auto-mated guided vehicle (AGV) with computer vision, is described. The white stripe line withcertain width is used as inductive mark for...The structure, function and working principle of JLUIV-3, which is a new typeof auto-mated guided vehicle (AGV) with computer vision, is described. The white stripe line withcertain width is used as inductive mark for JLUIV-3 automated navigation. JULIV-3 can automaticallyrecognize the Arabic numeral codes which mark the multi-branch paths and multi-operation buffers,and autonomously select the correct path for destination. Compared with the traditional AGV, it hasmuch more navigation flexibility and less cost, and provides higher-level intelligence. Theidentification method of navigation path by using neural network and the optimal control method ofthe AGV are introduced in detail.展开更多
For welding path determination,the use of vision sensors is more effctive compared with complex ofline programming and teaching in small to medium volume production.However,interference factors such as scratches and s...For welding path determination,the use of vision sensors is more effctive compared with complex ofline programming and teaching in small to medium volume production.However,interference factors such as scratches and stains on the surface of the workpiece may affect the extraction of weld information.In the obtained weld image,the weld seams have two distinct features related to the workpiece,which are continuous in a single process and separated from the workpiece's gray value.In this paper,a novel method is proposed to identify the welding path based on the region of interest(ROI)opera-tion,which is concentrated around the weld seam to reduce the interference of external noise.To complete the identi-fication of the entire welding path,a novel algorithm is used to adaptively generate a dynamic ROI(DROI)and perform iterative operations.The identification accuracy of this algorithm is improved by setting the boundary condi-tions within the ROI.Moreover,the experimental results confirm that the coefficient factor used for determining the ROI size is a pivotal influencing factor for the robustness of the algorithm and for obtaining an optimal solution.With this algorithm,the welding path identification accuracy is within 2 pixels for three common butt weld types.展开更多
基金This project is supported by National Natural Science Foundation of China(No.50175046) Technology Foundation of Education Ministry of China(No.00037).
文摘The structure, function and working principle of JLUIV-3, which is a new typeof auto-mated guided vehicle (AGV) with computer vision, is described. The white stripe line withcertain width is used as inductive mark for JLUIV-3 automated navigation. JULIV-3 can automaticallyrecognize the Arabic numeral codes which mark the multi-branch paths and multi-operation buffers,and autonomously select the correct path for destination. Compared with the traditional AGV, it hasmuch more navigation flexibility and less cost, and provides higher-level intelligence. Theidentification method of navigation path by using neural network and the optimal control method ofthe AGV are introduced in detail.
基金This study was supported by the Special Plan of Major Scientific Instruments and Equipment of the State(Grant No.2018YFF01013101)the National Natural Science Foundation of China(Grant Nos.51775322,61704102,and 61603237)Project named ktKey technology research and demonstration line construction of advanced laser intelligent manufacturing equipment from Shanghai Lingang Area Development Administration.
文摘For welding path determination,the use of vision sensors is more effctive compared with complex ofline programming and teaching in small to medium volume production.However,interference factors such as scratches and stains on the surface of the workpiece may affect the extraction of weld information.In the obtained weld image,the weld seams have two distinct features related to the workpiece,which are continuous in a single process and separated from the workpiece's gray value.In this paper,a novel method is proposed to identify the welding path based on the region of interest(ROI)opera-tion,which is concentrated around the weld seam to reduce the interference of external noise.To complete the identi-fication of the entire welding path,a novel algorithm is used to adaptively generate a dynamic ROI(DROI)and perform iterative operations.The identification accuracy of this algorithm is improved by setting the boundary condi-tions within the ROI.Moreover,the experimental results confirm that the coefficient factor used for determining the ROI size is a pivotal influencing factor for the robustness of the algorithm and for obtaining an optimal solution.With this algorithm,the welding path identification accuracy is within 2 pixels for three common butt weld types.
