Monocular Vision-Based Parameter Estimation for Mobile Robotic Painting

Zhao, Qijie; Li, Xianfa; Lü, Jianxia; Yi, Jingang

doi:10.1109/tim.2018.2878427

Cited by 22 publications

(9 citation statements)

References 29 publications

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“…While providing people with more choices of wall decoration materials, it adds infinite artistic charm to our home life and shows the fashionable characteristics of wall decoration that is both personalized and not extravagant. The geometric shape is one of the essential design elements in home decoration, but few people can apply geometric shapes so wonderful and illusory and can express such tension and a strong sense of rhythm [10]. In the process of style design, we abandoned the expression of traditional wallpaper but used geometric patterns that can make the color contrast stronger, in pure color or geometric form.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Computer Technology-Driven Mural Pattern Recognition Method

Wei

Gao

2022

Advances in Multimedia

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As an important part of cultural heritage, murals reflect the economy, culture, and ideas of different historical periods and are an important basis for historical research. The lines in murals are the core elements to express the beauty of images. They have an irreplaceable special position in murals and are of great significance in the protection and restoration of murals. With the development of image recognition technology, the recognition of mural images has become a key research topic. In recent years, as a new image processing technology, deep learning based on a convolutional neural network is widely used in many fields. Using a convolutional neural network to recognize images has become a very active topic. With the continuous deepening of the number of layers of the convolutional neural network model, its autonomous learning ability of image recognition continues to improve. However, there are still some problems in the current image recognition model based on a convolutional neural network for mural images with rich structural details and complex texture and color. Therefore, according to the texture and structural characteristics of mural images, this paper uses the design idea of a convolutional neural network for reference to carry out research on mural image recognition. The improved algorithm proposed in this paper is tested on the experimental data set of mural images. The experimental results show that the improved algorithm can reduce the recognition error; enhance the edge, texture, and structure information of the reconstructed mural image; and enrich the detail information of the reconstructed mural image.

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Section: Related Workmentioning

confidence: 99%

Intelligent Computer Technology-Driven Mural Pattern Recognition Method

Wei

Gao

2022

Advances in Multimedia

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“…The designed robot has a four DOF arm capable of painting different sides of the object. Zhao et al [9] Proposed a painting robot with a single camera. The estimation scheme for the robot painting parameter process has been implemented.…”

Section: Related Workmentioning

confidence: 99%

Guidance the Wall Painting Robot Based on a Vision System

Jasim¹,

Al-Isawi²,

Hamad³

2022

JESA

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The spraying operation is one of the most important processes in which industrial robots should be used, the most important of which is spraying for the purpose of painting walls, cars, and devices, in addition to spraying insecticides on plants to get rid of agricultural pests and others. An autonomous spraying robot is intended to alleviate numerous challenges associated with hand spraying. The proposed robot is a wall painting cartesian robot's conceptual design, which includes a paint object with a spray gun and a vision system. The cartesian robot has three links which are X, Y, and Z axes. The spray gun is connected to a screw, which causes the link to move linearly. When the spray gun reaches a particular limit, the camera detects it. The robot needs an appropriate trajectory to prevent collisions with other objects, pass a defined point in spatial coordinates, and accomplish rapid and precise mobility. Through the camera, the robot coordinates mapping, identifies non-sprayable places such as windows or doors and then inspects the spraying effect. The experimental results were applied to four maps (flat map, door map, window map, door and window map, and door and window map), and the corner locations for each map were identified using the vision system. Finally, by comparing the results to the actual distance, the lengths between the corners were computed.

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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%

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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Monocular Vision-Based Parameter Estimation for Mobile Robotic Painting

Cited by 22 publications

References 29 publications

Intelligent Computer Technology-Driven Mural Pattern Recognition Method

Intelligent Computer Technology-Driven Mural Pattern Recognition Method

Guidance the Wall Painting Robot Based on a Vision System

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

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