Robotics & Machine Learning Daily News2024,Issue(Feb.13) :56-57.DOI:10.1051/ijmqe/2023013

New Findings from Brunel University London in the Area of Robotics Published (Robotic path planning using NDT ultrasonic data for autonomous inspection)

Robotics & Machine Learning Daily News2024,Issue(Feb.13) :56-57.DOI:10.1051/ijmqe/2023013

New Findings from Brunel University London in the Area of Robotics Published (Robotic path planning using NDT ultrasonic data for autonomous inspection)

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Abstract

Current study results on robotics have been published. According to news reporting from Brunel University London by NewsRx journalists, research stated, “Robot deployed ultrasonic inspection for Non-Destructive Testing (NDT) offers several advantages including time efficiency gains, the reducing of repetitive manual workloads for operators and the enabling of inspection of environments hazardous to human health. Due to accuracy requirements, NDT robotic inspection has traditionally used the concept of digital twins for path planning activities.” The news editors obtained a quote from the research from Brunel University London: “Recent development has sought to automate this process through visual feedback using low-cost camera sensors. However, these methods do not take into account the use of NDT data itself as part of the robot path planning process. As a consequence, poor path planning accuracy can result due to the inability of conventional cameras to capture internal defects or geometric features. This paper introduces a novel concept of using the NDT ultrasonic data as part of a robotic path planning feedback loop. Firstly, the robot is manually positioned near the start of a weld, and the ultrasonic data is collected. Next, algorithms are implemented to monitor changes in the weld geometry, to determine the robot’s movement and pose based on real-time monitoring data, and to enable the robot to autonomously scan a weld with a minimum of operators input, path planning or digital twin. This is advantageous to NDT as visual sensors are unable to monitor geometric features within the weld.”

Key words

Brunel University London/Emerging Technologies/Machine Learning/Robot/Robotics/Robots

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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