QuicaBot: Quality Inspection and Assessment Robot

Yan, Rui-Jun; Kayacan, Erdal; Chen, I‐Ming; Tiong, Lee Kong; Wu, Jing

doi:10.1109/tase.2018.2829927

Cited by 54 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The robot can automatically move one glass facade panel to another panel without human assistance and also has 15 layers of a deep learning framework for detecting the crack on the glass panel. Quality inspection and assessment robot QuicaBot was reported by Yan et al [ 17 ]. The robot was used for quality assessment of buildings after construction and performed defect identification tasks, including hollowness, alignments, cracks, evenness, and inclination.…”

Section: Introductionmentioning

confidence: 99%

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Ramalingam

Tun²,

Mohan

et al. 2021

Sensors

View full text Add to dashboard Cite

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon." The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.

show abstract

Section: Introductionmentioning

confidence: 99%

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Ramalingam

Tun²,

Mohan

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…A delicate arm mechanism is designed for achieving full access to the walls of an entire room. In Yan et al (2019), a robot for post-construction quality assessment is developed. It is designed to complete quality inspection of five kinds of defects.…”

Section: Introductionmentioning

confidence: 99%

Robotized interior finishing operations with visual feedback

Jiang

2021

View full text Add to dashboard Cite

Purpose This paper aims to address the problem of integrating sensor feedback in robotized interior finishing operations. Its motivation is to finally realize automatic operations necessitating no human intervention. A vision-based approach is proposed for monitoring the execution status and changing the action accordingly. Design/methodology/approach First, a robotic system is proposed which can realize two typical interior finishing operations, namely, putty applying and wall sanding. Second, a new method based on a deep neural network is proposed to process the visual information capturing the execution status of the interior finishing operations. It helps to determine essential parameters on where should be processed and how to execute the corresponding operation. With the proposed method, vision information is embedded into the execution of interior finishing in a closed loop style. Findings The experiments demonstrate the feasibility of the proposal and reveal problems for further improvement of the autonomous interior finishing robot. Originality/value This provides an original insight into robotized interior finishing by addressing an attempt on integrating visual feedback into the manual process.

show abstract

“…Other designs of the pipeline inspection robots are also worthy for study [13][14][15][16]. In addition, many aspects should be carefully considered on the integration of robotic systems, such as intelligence [17,18], perception [19,20], task planning [21], robot control [22][23][24], fault tolerance [25,26], etc. All these aspects bring challenges to the successful development of robotic systems for oil pipeline inspection.…”

Section: Introductionmentioning

confidence: 99%

Development of a Pipeline Inspection Robot for the Standard Oil Pipeline of China National Petroleum Corporation

Zhang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

The periodic inspection for oil pipelines is required due to the deterioration over time. A multitude of factors brings such a deterioration, from corrosion, leaks, to cracks, which may lead to blowbacks and cause the damages for operators and the environments. With the progress of robotics technology, various types of mobile robots and mechanisms are designed to cope with this issue. Rather than the assignment of human workers in hazardous environments, the deployment of such kinds of inspection robots can take on this duty more time-efficiently and safely, preventing the human workers from the high-risk of the inspection task in the oil pipelines. This paper presents a novel design of a mobile robot for oil pipeline inspection, which is cooperated with the China National Petroleum Corporation (CNPC). With the improvement of the previous inspection robot used in CNPC’s standard oil pipelines, the newly designed robot is composed of six groups of symmetrical supporting wheels, and a more powerful motors as well as a more advanced control system. This new design endows the oil pipeline inspection robot with better performance on six aspects: traction, obstacle-adaptivity, operation endurance, gradeability, visual perception, and stability. The field testing results at multiple oil transfer stations across several months demonstrate the reliability of this mobile robot under various severe situations in China and validate its performance in the studied aspects.

show abstract

QuicaBot: Quality Inspection and Assessment Robot

Cited by 54 publications

References 22 publications

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Robotized interior finishing operations with visual feedback

Development of a Pipeline Inspection Robot for the Standard Oil Pipeline of China National Petroleum Corporation

Contact Info

Product

Resources

About