Trajectory Classification and Recognition of Planar Mechanisms Based on ResNet18 Network

Abstract

This study utilizes the ResNet18 network to classify and recognize trajectories of planar mechanisms. This research begins by deriving formulas for trajectory points in various typical planar mechanisms, and the resulting trajectory images are employed as samples for training and testing the network. The classification of trajectory images for both upright and inverted configurations of a planar four-bar linkage is investigated. Compared with AlexNet and VGG16, the ResNet18 model demonstrates superior classification accuracy during testing, coupled with reduced training time and memory consumption. Furthermore, the ResNet18 model is applied to classify trajectory images for six different planar mechanisms in both upright and inverted configurations as well as to identify whether the trajectory images belong to the upright or inverted configuration for each mechanism. The test results affirm the feasibility and effectiveness of the ResNet18 network in the classification and recognition of planar mechanism trajectories.