Robot-based precision recognition of small parcels images: methodology and experimental analysis

Abstract

With the increasing use of robots in various industries and daily life, researchers have been focusing on studying this area. In the chemical industry, where there are potential risks, using robots instead of manual labor in hazardous environments can effectively decrease the chances of accidents. However, there are still challenges in terms of low efficiency, poor accuracy, and incorrect positioning in robot sorting operations. To tackle these issues, a method based on the visual perception of information has been suggested. A prototype of a sorting robot verification experiment platform has been developed, which achieves precise sorting through graphic recognition and positioning of parcels. The experiment for robot parcel sorting has been conducted, and the results are promising. The adaptive recognition rate for miscellaneous feature package images is 96.31%, with an average time of 0.12 seconds per image. These outcomes demonstrate the successful implementation of robot parcel sorting. Overall, the use of robots in hazardous environments significantly reduces the risk of accidents. The proposed method based on visual information perception has shown promising results in enhancing the efficiency, accuracy, and positioning of robot sorting operations.