The production process of robot castings mainly includes processes such as robot core taking, core assembly, core setting, dipping, pouring, sand casting, polishing, and cleaning. The handling process involves every process of the casting production line and is one of the key processes of the casting production line. For the 6-axis robot used for handling, the kinematics model of the robot is carried out according to the improved DH parameter method, and the dynamic model of the robot is established by using Newton-Euler equation and Lagrangian equation respectively. Secondly, according to the requirements of casting handling technology, the motion trajectory is planned according to the established kinematics model and dynamics model, and the dynamic simulation is carried out with MATLAB. The joint torques of the robot dynamic models established by the two methods are very close, which verifies the correctness of the dynamic model.
There are many redundant parameters in Convolutional Neural Network(CNN) when it is used for target recognition in a specific scene, which will greatly occupy the calculation amount and affect the operating efficiency of software and hardware, and cannot meet the real-time target detection requirements of the algorithm in a specific scene. In this paper, channel pruning, layer pruning and their hybrid pruning experiments were carried out on you only look once version 3(YOLOv3), a typical CNN target recognition model. The result of the hybrid pruning can greatly reduce the model parameters and the amount of calculation, and can reduce the model of resource utilization through the comparative analysis. And the volume of the model after hybrid pruning was reduced 94.4% when mAP only loss 0.9%. The model inference time was reduced by 36.6%. This study could provide references for the optimization of object recognition model in structured scenes such as road and workshop.
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