An Intelligent Autonomous Document Mobile Delivery Robot Using Deep Learning

An Intelligent Autonomous Document Mobile Delivery Robot Using Deep Learning

Autonomous Document

Deep Learning

Mobile Selivery Robot

This paper presents an intelligent autonomous document mobile delivery robot using a deep learning approach. The robot is built as a prototype for document delivery service for use in offices in which it can adaptively move across different surfaces, such as terrazzo, canvas, and wooden. In this work, we introduce a convolutional neural network (CNN) to recognize the traffic lanes and the stop signs with the assumption that all surfaces have identical traffic lanes. We train the model using a custom indoor traffic lane and stop sign dataset with the label of motion directions. CNN extracts a direction-of-motion feature to estimate the robot's direction and to stop the robot based on an input image monocular camera view. These predictions are used to adjust the robot's direction and speed. The experimental results show that this robot can move across different surfaces along with the same structured traffic lanes, achieving the model accuracy of 96.31%. The proposed robot helps to facilitate document delivery for office workers, allowing them to work on other tasks more efficiently.

Ganokratanaa, Thittaporn

Ketcham, Mahasak

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 21 (2022); pp. 4-22

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-11-15

Copyright (c) 2022 Mahasak Ketcham, Thittaporn Ganokratanaa

https://creativecommons.org/licenses/by/4.0

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article