An autonomous vision-based shelf-reader robot using faster R-CNN

Abstract

Purpose The purpose of this study is to design, program and implement an intelligent robot for shelf-reading. an essential task in library maintenance is shelf-reading, which refers to the process of checking the disciplines of books based on their call numbers to ensure that they are correctly shelved. Shelf-reading is a routine yet challenging task for librarians, as it involves controlling call numbers on the scale of thousands of books promptly. Design/methodology/approach Leveraging the strength of autonomous robots in handling repetitive tasks, this paper introduces a novel vision-based shelf-reader robot, called \emph{Pars} and demonstrate its effectiveness in accomplishing shelf-reading tasks. Also, this paper proposes a novel supervised approach to power the vision system of \emph{Pars}, allowing it to handle motion blur on images captured while it moves. An approach based on Faster R-CNN is also incorporated into the vision system, allowing the robot to efficiently detect the region of interest for retrieving a book’s information. Findings This paper evaluated the robot’s performance in a library with $120,000 books and discovered problems such as missing and misplaced books. Besides, this paper introduces a new challenging data set of blurred barcodes free publicly available for similar research studies. Originality/value The robot is equipped with six parallel cameras, which enable it to check books and decide moving paths. Through its vision-based system, it is also capable of routing and tracking paths between bookcases in a library and it can also turn around bends. Moreover, \emph{Pars} addresses the blurred barcodes, which may appear because of its motion.