What are you looking for today?

Wang, Wei, Gope, Prosanta and Cheng, Yongqiang (2022) An AI-Driven Secure and Intelligent Robotic Delivery System. Transactions on Engineering Management. pp. 1-16. ISSN 1558-0040

Document

  • [thumbnail of Author accepted manuscript on publisher template]
    Author accepted manuscript on publisher template
    An_AI-Driven_Secure_and_Intelligent_Robotic_Delivery_System.pdf - Accepted Version

Abstract

Abstract

Last-mile delivery has gained much popularity in recent years, it accounts for about half of the whole logistics cost. Unlike container transportation, companies must hire significant number of employees to deliver packages to the customers. Therefore, many companies are studying automated methods such as robotic delivery to complete the delivery work to reduce the cost. It is undeniable that the security issue is a huge challenge in such a system. In this article, we propose an AI-driven robotic delivery system, which consists of two modules. A multilevel cooperative user authentication module for delivering parcel using both PIN code and biometrics verification, i.e., voiceprint and face verification. Another noncooperative user identification module using face verification which detects and verifies the identification of the customer. In this way, the robot can find the correct customer and complete the delivery task automatically. Finally, we implement the proposed system on a Turtlebot3 robot and analyze the performance of the proposed schema. Experimental results show that our proposed system has a high accuracy and can complete the delivery task securely.

Item Type: Article
Depositing User: Yongqiang Cheng
URI: https://sure.sunderland.ac.uk/id/eprint/16819

Further Details

More Information

Depositing User

Identifiers

Item ID
16819
Identification Number
DOI: 10.1109/TEM.2022.3142282
ISSN
1558-0040

Users with ORCIDS

ORCID for Yongqiang Cheng

Catalogue record

Date Deposited
11 Jan 2024 12:17
Last Modified
04 Jun 2025 14:58

Actions (login required)

View Item (Repository Staff Only)

Downloads per month over past year