Robotized interior finishing operations with visual feedback

Jiang, Xin; Li, Xiang

doi:10.1108/ir-02-2021-0034

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…This approach, with the inclusion of identified defect data into the building information model, markedly enhances the efficacy of construction project management. Furthermore, CNNs (Bard et al, 2019; Jiang & Li, 2022; Zhang et al, 2019) have been employed to detect and assess surface quality. These methodologies serve as valuable references for the monitoring of plastering quality.…”

Section: Related Workmentioning

confidence: 99%

Puttybot: A sensorized robot for autonomous putty plastering

Liu,

Chen,

Eldosoky

et al. 2024

Journal of Field Robotics

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Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force‐controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large‐scale wall was processed without human involvement.

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Section: Related Workmentioning

confidence: 99%

Puttybot: A sensorized robot for autonomous putty plastering

Liu,

Chen,

Eldosoky

et al. 2024

Journal of Field Robotics

Self Cite

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“…Elbehiery et al 23 devised a control method that integrates image processing and morphological operation techniques to evaluate the surface quality of tiles. Furthermore, Convolutional Neural Networks 24,17,25 have been employed to detect and assess surface quality. These methodologies serve as valuable references for the monitoring of plastering quality.…”

Section: Plastering Machines/robotsmentioning

confidence: 99%

Puttybot - A sensorized robot for autonomous putty plastering

Liu,

Chen,

Eldosoky

et al. 2023

Preprint

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Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, lift platform, and a macro/micro manipulator. The force-controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes Convolutional Neural Networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm’s effectiveness was validated during an autonomous plastering trial wherein a large-scale wall was processed without human involvement.

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“…However, both of these works only consider plaster spraying. For trowelling, the primary investigation has been in training a convolutional neural network to predict trowelling directions based on images of the surface [15], [16]. Since little prior work has been conducted on path planning for robotic plaster trowelling, this is an unexplored field open to investigation.…”

Section: A Related Workmentioning

confidence: 99%

PLASTR: Planning for Autonomous Sampling-Based Trowelling

Kuhlmann-Jørgensen,

Pankert,

Pietrasik

et al. 2023

IEEE Robot. Autom. Lett.

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Plaster is commonly used in the construction industry to finish walls and ceilings, but the application is labor-intensive and physically strenuous, which motivates the need for automation. We present PLASTR, a receding horizon optimization-based planning algorithm for robotic plaster trowelling. It samples trowelling sequence rollouts from a new plaster simulator and weights them according to the flatness of the finished wall. The proposed simulator approximates the real-world plastertrowel interaction adequately while allowing execution orders of magnitude faster than real-time. We evaluate PLASTR in simulation and on a real-world test setup and compare it to two handcrafted heuristic baseline algorithms. PLASTR performs equal to or better than the best heuristic in terms of material coverage for both simulated and real-world experiments while being 50 % more efficient in terms of trowelled distance.

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Robotized interior finishing operations with visual feedback

Cited by 4 publications

References 17 publications

Puttybot: A sensorized robot for autonomous putty plastering

Puttybot: A sensorized robot for autonomous putty plastering

Puttybot - A sensorized robot for autonomous putty plastering

PLASTR: Planning for Autonomous Sampling-Based Trowelling

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