Automation in loading/unloading semi-knockdown vehicles in assembly plants for intelligent navigation

Adeyinka Oluwo; Muhiz Ambali; Sunday Ayoola Oke; Henry Omoregbe; Bayo Ogunmola; David Livingstone; Francis  Onoroh

doi:10.29194/NJES.29010152

Authors

Adeyinka Oluwo Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
Muhiz Ambali Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
Sunday Ayoola Oke Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
Henry Omoregbe Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
Bayo Ogunmola Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
David Livingstone Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.
Francis Onoroh Department of Mechanical Engineering, University of Lagos, Lagos, Nigeria.

DOI:

https://doi.org/10.29194/NJES.29010152

Keywords:

Automated Carrier, Semi-Knockdown Vehicles, Fuzzy Logic Control Unit, Automated Carrier Prototype Design

Abstract

Manual handling of semi-knockdown vehicles in assembly plants is unsafe, time-consuming, inefficient, and prone to quality irregularities. To intervene in addressing these problems, this study develops a prototype of an automated load carrier intelligent navigator. The work centre is analysed for space, material type and handling requirements. This is followed by design and testing, whereby software, hardware and mechanical engineering are integrated in the context of process optimisation. The prototype was tested on rough and smooth surfaces, for no-obstacle and obstacle avoidance conditions. On rough and smooth surfaces with no obstacles, the minimum distance considered is 0.5m, and the average speed and time determined are 0.08m/s and 6.23s, 0.17m/s and 2.97s, respectively. For the maximum distance of 3.0m, the average speeds and times determined are 0.081 m/s and 37.42s, and 0.18 m/s and 17.35s, respectively. The average distance considered for both rough and smooth surfaces is 1.75 m, and the average speed and time at each scenario are 0.081 m/s, 21.78s, and 0.17 m/s, 10.26s. The voltage of the battery drops, with a corresponding decrease in the speed of the motors. The automated carrier prototype makes the best decisions when it encounters an obstacle, giving the best outputs. This paper contributes by providing real-time intelligent navigation data and accurate regulation of the automated carrier for automotive assembly plants. Its novelty lies in conducting experimental investigations using the automated loading/unloading intelligent navigator to explore its advantages compared to manual loading/unloading in automotive assembly plants. In conclusion, building a carrier for assembly operations enhances assembly operational performance, correcting inefficient and unsafe loading and unloading processes.

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