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Modern Port Logistics and Related Traffic

Undergraduate #346
Discipline: Technology and Engineering
Subcategory: Computer Engineering

Brandon Harrington - Virginia State University
Co-Author(s): Pedro Tele, Federal University of Sergipe, Brazil Guillermo Medeiros, Federal University of Sao Joao del-Rei, Brazil



A container terminal is a facility where cargo containers are transferred between different transport vehicles, for onward transportation. Congestion at container terminals occurs due to high volumes of cargo containers waiting to be separated with a limited amount of man power. In this work, an autonomous robot is designed with the intention of simulating modern port logistics and its related traffic. The purpose of this design is to increase efficiency of container terminals by increasing the rate at which cargo containers are separated. To simulate modern port logistics, an 8’ x 8’ course that is modeled after the Hampton Roads Shipping Container Terminal is constructed.

The objective of the autonomous robot is to successfully detect shipping goods on a barge and transport the goods to the correct shipping zone (rail, boat, or truck). The rail shipping zone consists of four different colors (red, green, yellow, and blue). The objective is achieved by designing a robot with the following constraints: dimensions are less than 12” x 12” x 12”, without the mechanical arm extended. When the arm is extended, the depth and width of the robot are less than 20 inches. The robot contains a start button that enables the robot to detect and read the Quick Response (QR) codes that are placed on the containers. Using its mechanical arm, the robot gains possession of the containers one by one and delivers it to the correct shipping zone. Correct shipping zones are determined from decoded QR codes. Simulation of modern port logistics and related traffic will open doors to a future of autonomous container terminals.

Funder Acknowledgement(s): HBCU-UP

Faculty Advisor: Ali Ansari, aansari@vsu.edu

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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