RoboPainter — A detailed robot design for interior wall painting

Sorour, Mohamed

doi:10.1109/arso.2015.7428214

Cited by 19 publications

(5 citation statements)

References 7 publications

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“…Construction robots often encounter plane identification problems, such as those found in wall painting robots [21], floor surface profiling robots [22], ground plane detection [23][24][25], and surface reconstruction [26,27]. Methods for plane extraction and parameter identification include random sample consensus (RANSAC) and its variants.…”

Section: Introductionmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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Stereo vision allows machines to perceive their surroundings, with plane identification serving as a crucial aspect of perception. The accuracy of identification constrains the applicability of stereo systems. Some stereo vision cameras are cost-effective, compact, and user-friendly, resulting in widespread use in engineering applications. However, identification errors limit their effectiveness in quantitative scenarios. While certain calibration methods enhance identification accuracy using camera distortion models, they rely on specific models tailored to a camera’s unique structure. This article presents a calibration method that is not dependent on any particular distortion model, capable of correcting plane position and orientation identified by any algorithm, provided that the identification error is biased. A high-precision mechanical calibration platform is designed to acquire accurate calibration data while using the same detected material in real measurement scenarios. Experimental comparisons confirm the efficacy of plane pose correction on PCL-RANSAC, with the average relative error of distance reduced by 5.4 times and the average absolute error of angle decreasing by 41.2%.

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Section: Introductionmentioning

confidence: 99%

Increased plane identification precision with stereo identification

Zhao

2023

Robotica

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“…Early research works (Aris, 2005; Naticchia et al, 2006, 2007; Rosenfeld et al, 1991) lacked external sensors to autonomously perceive interior walls. Recently low‐cost ultrasonic arrays (Megalingam et al, 2020; M. Sorour, 2015) were employed to measure the position of each wall plane and a vision‐based estimator using a monocular camera and four laser distance sensors (Zhao et al, 2018) was proposed to measure the position and orientation of each wall plane. These two approaches were unable to measure the dimensions of interior walls.…”

Section: Introductionmentioning

confidence: 99%

“…Several interior painting robots have been proposed since the 1990s (Aris, 2005; Naticchia et al, 2006, 2007; Rosenfeld et al, 1991), which generally consisted of a mobile base and a manipulator. Recently, a roller‐based robot comprising a three degrees‐of‐freedom (3‐DOF) mecanum wheeled mobile base and a 2‐DOF robot arm was proposed to automatically paint a single wall plane (M. Sorour, 2015; M. T. Sorour et al, 2011). The mecanum wheeled mobile base was easy to slip on the ground and the robot arm only adapted to painting walls under 2.7 m height.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a roller-based robot comprising a three degrees-offreedom (3-DOF) mecanum wheeled mobile base and a 2-DOF robot arm was proposed to automatically paint a single wall plane (M. Sorour, 2015; M. T. Sorour et al, 2011). The mecanum wheeled mobile base was easy to slip on the ground and the robot arm only adapted to painting walls under 2.7 m height.…”

mentioning

confidence: 99%

“…Recently low-cost ultrasonic arrays (Megalingam et al, 2020;M. Sorour, 2015) were employed to measure the position of each wall plane and a vision-based estimator using a monocular camera and four laser distance sensors (Zhao et al, 2018) was proposed to measure the position and orientation of each wall plane.…”

mentioning

confidence: 99%

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Building information modeling‐based 3D reconstruction and coverage planning enabled automatic painting of interior walls using a novel painting robot in construction

Zhou

Yue

et al. 2022

Journal of Field Robotics

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Due to the lack of experienced labor and high‐quality requirements, interior painting task calls for robotic solutions in the construction field. This paper presents a robotic system aiming for the automatic interior wall painting task. The new painting robot comprises an omnidirectional mobile base and a seven degrees‐of‐freedom (7‐DOF) redundant manipulator consisting of a 6‐DOF robot arm and a 1‐DOF lifting mechanism to make the painting task more flexible. Further, a building information modeling‐based three‐dimensional (3D) reconstruction approach is used to obtain the complete 3D model of all walls in the interior environment for automatic painting. Moreover, we propose a two‐stage coverage planning framework to automatically generate optimal mobile base paths and manipulator trajectories to paint the walls. In the proposed framework, the global planner plans the painting waypoints sequence optimally. The local planner generates the mobile base poses by a new evaluation function which both ensures coverage of all painting waypoints and optimizes robot paths length. The results of field tests showed that the whole painting robot system has high environment adaptability to paint interior walls automatically. Furthermore, the planning results can significantly reduce total robot paths length compared with the previous studies. The painting robot can automatically finish a painting of 46.3 m2 ${{\rm{m}}}^{2}$ in 59.3 min, and this performance verifies the proposed design and algorithms.

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