BIM-Integrated Collaborative Robotics for Application in Building Construction and Maintenance

Follini, Camilla; Magnago, Valerio; Freitag, Kilian; Terzer, Michael; Marcher, Carmen; Riedl, Michael; Giusti, Andrea; Matt, Dominik T.

doi:10.3390/robotics10010002

Cited by 47 publications

(13 citation statements)

References 33 publications

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“…A selection of input and output parameters is given in Table 1. If a BIM-model already exists or if there are further paths to be planned for following tasks that should also be carried out by robots, the path planning can be done using the BIM-model [16][17][18]. The aim of this research is to determine the parameter settings necessary for different requirements and then to be able to create point clouds according to needs via automated planning (and autonomous robots).…”

Section: Fig 2 2d Floor Plan Of a Parking Structure Shown As A Bitmapmentioning

confidence: 99%

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Quantified point clouds and enriched BIM-Models for digitalised maintenance planning

Morgenstern

Raupach

2022

MATEC Web Conf.

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Digitisation in the construction industry continues to advance and, together with the increasing dissemination and further development of hardware and software, is steadily opening up further opportunities for innovative ways of working. Building Information Modelling (BIM) is currently becoming the standard for new construction but has not yet been optimised for use in existing buildings. Therefore, the Institute of Building Materials Research (ibac) is researching new methods and possibilities for BIM-based building preservation. In this paper, the automated creation and analysis of point clouds as well as the implementation of further information from in situ diagnosis and monitoring systems in BIM-Models are presented. On a practical example, the different steps of a subsequent digitisation of an existing building are demonstrated considering new possibilities as autonomous robots and the intelligent utilisation of sensors and diagnostics tools. The goal is a decision support tool, which is independent from proprietary software, adaptive to different types of buildings and open for various interfaces. Current results show that quantifying point clouds and making BIM-models usable beyond the planning and execution phase for new buildings are essential steps for the digitisation of building maintenance. The proposed digital workflow holds great potential for effective building diagnoses and efficient service life management.

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Section: Fig 2 2d Floor Plan Of a Parking Structure Shown As A Bitmapmentioning

confidence: 99%

mentioning

confidence: 99%

Quantified point clouds and enriched BIM-Models for digitalised maintenance planning

Morgenstern

Raupach

2022

MATEC Web Conf.

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“…The construction robot is an intelligent construction equipment that can improve the working environment and increase efficiency. Considering the building process in the construction industry is complex, there have been a multitude of intelligent construction robots that assist in construction, such as wall construction robots (Han et al, 2006), painting robots (Asadi et al, 2018), and maintenance building robots (Follini et al, 2020). Construction robots can improve construction efficiency, ensure the health and safety of construction workers, and solve the labor shortage in the construction industry (Mascaro et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Digital twin‐assisted fault diagnosis system for robot joints with insufficient data

Song

Shi

Bai

et al. 2022

Journal of Field Robotics

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The robot joint is an important component of the construction robot, and its fault diagnosis can ensure the exact execution of building jobs, stable operation, and timely prevention of probable safety mishaps. However, deep learning‐based fault diagnosis needs a multitude of measured fault data, which is difficult to obtain for various reasons. To solve the problem of insufficient data, a digital twin‐assisted fault diagnosis system for robot joints is proposed. First, a simplified dynamics model of the robot joint is developed to generate the virtual entity data which can be used as the X‐domain data for the digital twin model. Second, a CycleGAN‐based digital twin model is proposed to map the virtual entity (X‐domain) data to the physical entity (Y‐domain) utilizing only a small amount of measured data. In the end, a test‐rig for the robot joint is built to simulate the robot's working conditions, and the CNN‐ResNet classifier is utilized to verify the effectiveness of the simulated data generated by the digital twin model. The results show that the fault diagnosis accuracy can be increased from 32.5% to 98.86% utilizing only 400 sets of measured data.

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“…Recent advancements in robotics and autonomous systems have resulted in an increase in autonomous capabilities and the use of more intelligent machines in a wider range of applications [1][2][3][4]. Gripping various geometric objects is a key competency for robots to achieve more general-purpose functionality [5].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

Sayour

Kozhaya

Saab

2022

Journal of Robotics

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Recent advancement in vision-based robotics and deep-learning techniques has enabled the use of intelligent systems in a wider range of applications requiring object manipulation. Finding a robust solution for object grasping and autonomous manipulation became the focus of many engineers and is still one of the most demanding problems in modern robotics. This paper presents a full grasping pipeline proposing a real-time data-driven deep-learning approach for robotic grasping of unknown objects using MATLAB and convolutional neural networks. The proposed approach employs RGB-D image data acquired from an eye-in-hand camera centering the object of interest in the field of view using visual servoing. Our approach aims at reducing propagation errors and eliminating the need for complex hand tracking algorithm, image segmentation, or 3D reconstruction. The proposed approach is able to efficiently generate reliable multi-view object grasps regardless of the geometric complexity and physical properties of the object in question. The proposed system architecture enables simple and effective path generation and a real-time tracking control. In addition, our system is modular, reliable, and accurate in both end effector path generation and control. We experimentally justify the efficacy and effectiveness of our overall system on the Barrett Whole Arm Manipulator.

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BIM-Integrated Collaborative Robotics for Application in Building Construction and Maintenance

Cited by 47 publications

References 33 publications

Quantified point clouds and enriched BIM-Models for digitalised maintenance planning

Quantified point clouds and enriched BIM-Models for digitalised maintenance planning

Digital twin‐assisted fault diagnosis system for robot joints with insufficient data

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

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