Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering

Zhang, Jianqi; Xu, Yang; Wang, Wei; Guan, Jinchao; Ding, Ling; Lee, Vincent C. S.

doi:10.1016/j.autcon.2022.104699

Cited by 68 publications

(25 citation statements)

References 117 publications

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“…Already today, many AI examples can be found, in which robots are used on construction sites. [33][34][35] However, widespread application of digital or intelligent machines has only been observed in some occasional projects. Even though an adequate number of technologies are already very advanced, many of them lack practical realization; the existing approaches are often in early stages of only a few research projects.…”

Section: Applications In Aec: Construction Robots and Other Examplesmentioning

confidence: 99%

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Self‐organizing systems in the construction industry

Lohmann

Kölzer

Schaan

et al. 2023

Engineering Reports

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Self‐organization is found in numerous natural and artificial systems. For the construction industry, self‐organizing processes are not only applicable in managing or organizing tasks, they also provide insight into materials or can be implemented into operations on building sites. In this paper, the terms feedback, agents, emergence and self‐organization are described to provide an insight into the complex processes relevant to a lot of fields in the construction industry. For this, the principles of adaptive processes are transferred to artificial systems. Besides self‐organization in building materials, an outlook is given to describe construction sites as cyber‐physical systems. Inside such systems, agents learn to organize work tasks more efficiently, particularly with the help of reinforcement learning. In this regard, a transfer of natural self‐organization—which also exist in materials—to building processes is made. A combination of natural and artificial systems is presented to promote self‐organization for various fields of application in the construction industry, especially for building sites.

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Section: Applications In Aec: Construction Robots and Other Examplesmentioning

confidence: 99%

“…Already today, many AI examples can be found, in which robots are used on construction sites 33–35 . However, widespread application of digital or intelligent machines has only been observed in some occasional projects.…”

Section: Applications In Aec: Construction Robots and Other Examplesmentioning

confidence: 99%

Self‐organizing systems in the construction industry

Lohmann

Kölzer

Schaan

et al. 2023

Engineering Reports

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“…This impact becomes more pronounced with longer transportation times [1]. As production systems become more complex and labor costs increase, factories are increasingly inclined to use Automated Guided Vehicles (AGVs) for material transportation [2]. Therefore, researching the job shop scheduling problem integrated multiple AGVs holds significance.…”

Section: Introductionmentioning

confidence: 99%

A deep reinforcement learning method for job shop scheduling integrated with automated guided vehicles

Huang,

Du,

Rong

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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With the emergence of the smart factory concept, Automated Guided Vehicles have been widely utilized in job shop transportation systems, drawing increased attention to the job shop scheduling problem integrated with multiple AGVs. In this problem, the sequential-logic constraint between transportation and processing tasks in each operation complicates the problem's decoupling compared to traditional job shop scheduling problems. In this paper, a deep reinforcement learning-based method is proposed to address this integrated scheduling problem. The problem is initially formulated as a Markov Decision Process, with state features designed from the perspectives of jobs and AGVs. Furthermore, a novel reward function is designed to maximize job shop resource utilization. The primary objective of the scheduling is to obtain an optimal solution that maximizes resource utilization, thereby enhancing the core competitiveness of the factory. Benchmark experiments demonstrate the effectiveness and competitiveness of this method in obtaining solutions for the integrated problems.

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“…Road roller route tracking using a thermal-based technique that integrates intelligent compaction with a global positioning system was proposed (Lu et al, 2021). Zhang et al (2023) summarized the automatic guided vehicle (AGV) and AMR in civil engineering. It is pointed out that with the development of AGV/AMR technology, the technology will be applied.…”

Section: Introductionmentioning

confidence: 99%

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

Zhang,

Yang,

Wang

et al. 2023

Computer aided Civil Eng

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Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the heavy equipment used. In this article, a cross‐entropy‐based adaptive fuzzy control (CEAFC) method is proposed, which reaches visual tracking with unmanned mobile robot (VT‐UMbot) for road cracks. Specifically, the CEAFC method uses cross‐entropy optimization iteration to tune parameters for the tracking controller, and fuzzy logic is constructed to explore robustness improvement. Moreover, a framework of VT‐UMbot based on a four‐wheel independent differential drive is established, and visual servo and tracking control are integrated into the system. Our experiment shows that the proposed method is extensively evaluated on three road cracks scenarios and achieves state‐of‐the‐art performance with high efficiency.

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Automated guided vehicles and autonomous mobile robots for recognition and tracking in civil engineering

Cited by 68 publications

References 117 publications

Self‐organizing systems in the construction industry

Self‐organizing systems in the construction industry

A deep reinforcement learning method for job shop scheduling integrated with automated guided vehicles

Cross‐entropy‐based adaptive fuzzy control for visual tracking of road cracks with unmanned mobile robot

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