Mobile robotics and 3D printing: addressing challenges in path planning and scalability

Mohammad Rastegarpanah; Mohammed Eesa Asif; Javaid Butt; Holger Voos; Alireza Rastegarpanah

doi:10.1080/17452759.2024.2433588

Mobile robotics and 3D printing: addressing challenges in path planning and scalability

Mohammad Rastegarpanah, Mohammed Eesa Asif, Javaid Butt, Holger Voos, Alireza Rastegarpanah^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

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Abstract

Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.

Original language	English
Article number	e2433588
Number of pages	44
Journal	Virtual and Physical Prototyping
Volume	19
Issue number	1
Early online date	2 Dec 2024
DOIs	https://doi.org/10.1080/17452759.2024.2433588
Publication status	E-pub ahead of print - 2 Dec 2024

Bibliographical note

Copyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Keywords

additive manufacturing
artificial intelligence
industry 4.0
Mobile robots
path planning

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10.1080/17452759.2024.2433588Licence: CC BY 4.0

Mobile robotics and 3D printing addressing challenges in path planning and scalability
Copyright © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Final published version, 1.98 MBLicence: CC BY 4.0

Cite this

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abstract = "Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.",

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AB - Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.

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Mobile robotics and 3D printing: addressing challenges in path planning and scalability

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