Introducing adaptive mechatronic designs in bulk handling industry

Kol, Tessa V. van; Jovanova, Jovana; Mohajeri, M. Javad; Schott, Dingena L.

doi:10.1109/aim46487.2021.9517377

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…This enables an automated operational solution through intelligent learning. Similarly, Tessa V van Kol et al provide information about an intelligent grabbing system that refers to the system of Bewley et al [26,27]. Their system is used to calculate the load volume in the bucket using point cloud data and the iterative closest point algorithm.…”

Section: Results and Discussion Of A Working Point Extraction Modelmentioning

confidence: 99%

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Ngo,

Lee,

Kim

et al. 2024

JMSE

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In seaports, the automatic Grab-Type Ship Unloader (GTSU) stands out for its ability to automatically load and unload materials, offering the potential for substantial productivity improvement and cost reduction. Developing a fully automatic GTSU, however, presents a unique challenge: the system must autonomously determine the position of the cargo hold and the coordinates of the working point and identify potential hazards during material loading and unloading. This paper proposes AI models designed to detect cargo holds, extract working points, and support collision risk warnings, utilizing both the LiDAR sensor and the camera in the GTSU system. The model for cargo hold detection and collision warning was developed using image data of the cargo hold and grab, employing the You Only Look Once model. Concurrently, the model responsible for extracting the coordinates of working points for the GTSU system was designed by integrating the cargo hold detection and point cloud processing models. After testing the AI models for the lab-scale GTSU, the results show that the cargo hold detection and collision warning models achieve an accuracy of approximately 96% and 90%, respectively. Additionally, the working point coordinates extracted from the sensor system show a deviation of 5–10% compared to traditional measurements.

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Section: Results and Discussion Of A Working Point Extraction Modelmentioning

confidence: 99%

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Ngo,

Lee,

Kim

et al. 2024

JMSE

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show abstract

“…Cameras have not only enhanced security but also signi cantly improved operational e ciency by enabling real-time monitoring of equipment, cargo ow, and operational quality. Additionally, they play a pivotal role in promoting sustainability (Van Kol et al, 2021). Their ability to collect valuable data has the potential to drive substantial gains in e ciency, safety, and quality (Pagano et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Emerging Technologies and Sustainability in Brazilian Ports and Terminals: An Analysis of the Current Situation

Resende,

Cunha,

Porte

et al. 2023

Preprint

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In the face of the growing demand for maritime transportation in the global economic context, ports are under increasing pressure from stakeholders to enhance their operational effectiveness and address environmental issues such as air and water pollution and inherent energy and security challenges. The transition to smarter and environmentally responsible ports is perceived not only as a choice but a strategic necessity, considering the multifaceted demands of the current global landscape. The Sustainable Development Goals offer a comprehensive framework to guide this evolution. In the Brazilian context, this paper aims to explore the impact of emerging technologies on the sustainability of Brazilian port industry. The research explores how such technological advancements influence the sustainability of the Brazilian port sector, considering their potential to drive sustainable practices and enhance operational efficiency. The study focused on Brazilian ports and terminals with the highest cargo volumes, including a sample of 23 port entities. The methodology adopted consisted of content analysis of sustainability reports published by the study's companies. An analysis of the sustainability reports of Brazilian ports and terminals reveals a diversity of technologies in continuous application. Technologies such as cameras, radar systems, artificial intelligence, and vessel traffic services, among others, are assuming multifaceted roles encompassing environmental monitoring, maritime traffic management, operational optimization, and security assistance. These technological advancements bolster the efficiency and safety of port operations and contribute significantly to sustainability efforts by facilitating early issue detection and mitigating environmental impacts. Furthermore, they create a pathway for the integration of emerging technologies like the Internet of Things and machine learning, offering the promise of additional efficiency gains and innovative solutions within the port sector.

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Introducing adaptive mechatronic designs in bulk handling industry

Cited by 2 publications

References 24 publications

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Design of an AI Model for a Fully Automatic Grab-Type Ship Unloader System

Emerging Technologies and Sustainability in Brazilian Ports and Terminals: An Analysis of the Current Situation

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