Deep learning-based method for recognizing continuous shovel loading positions in autonomous loader

A deep learning-based method for recognizing continuous shovel loading positions in an autonomous loader, comprising:

• acquiring laser point cloud data representing surface topography of a stockpile, and processing the laser point cloud data to obtain a plurality of shovel-loading region features of the stockpile;
• constructing a model of an actuating mechanism for a loader, performing a simulation force analysis on the model of the actuating mechanism and the plurality of shovel-loading region features of the stockpile, to obtain shovel-loading position features of the stockpile;
• analyzing and evaluating the shovel-loading position features of the stockpile, to obtain an optimal shovel-loading position feature of the stockpile;
• and
• constructing a detection model, acquiring an image of the stockpile to be identified simultaneously, inputting the image of the stockpile to be identified into the detection model for processing to obtain image processing results, and detecting and adjusting a shovel-loading position of the loader based on the image processing results and the optimal shovel-loading position feature.