A framework of using customized LIDAR to localize robot for nuclear reactor inspections
Zhang, Dayi and Cao, Jianlin and Dobie, Gordon and MacLeod, Charles Norman (2022) A framework of using customized LIDAR to localize robot for nuclear reactor inspections. IEEE Sensors Journal, 22 (6). pp. 5352-5359. ISSN 1530-437X (https://doi.org/10.1109/JSEN.2021.3083478)
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Abstract
While remote inspection of industrial structures, such as nuclear reactors, using robotic crawlers currently presents significant advantages in terms of safety, accuracy and cost, other challenges emerge due to poor context-awareness and positional accuracy. This results in a lack of visibility for path planning and difficulty in precise localization of NDE (Non-Destructive Evaluation) inspection data. LIDAR (Light Detection and Ranging) are one form of sensors that estimate distances at various angles to map the surrounding environment using optical techniques. Existing commercial LIDARs offer a long range of measurement, allowing mapping of the surroundings. However, such sensors often have centimeter accuracy and a minimum scan range, resulting in a blind area and are generally unsuitable for compact spaces and areas with high density of neighboring objects. This paper presents a framework for using a customized 2D laser scanner, an IMU (Inertial Measurement Unit) and a data fusion approach for localization inside high-density volumes such as nuclear reactors. The laser scanner offers precise measurements with submillimeter accuracy for items located in the short range. The IMU calculates the robot attitude angles, which are critical for inclination angle corrections. The facilities are often made of metallic materials with highly reflective surfaces, which remains problematic for the laser scanner. A mock-up nuclear dome, of realistic material construction, was utilized to benchmark the performance of this framework. The distance and orientation error observed were below 2 mm and 1°, respectively. The framework will be further processed to produce a close-range environment mapping.
ORCID iDs
Zhang, Dayi ORCID: https://orcid.org/0000-0003-4611-4161, Cao, Jianlin ORCID: https://orcid.org/0000-0003-1132-8784, Dobie, Gordon ORCID: https://orcid.org/0000-0003-3972-5917 and MacLeod, Charles Norman ORCID: https://orcid.org/0000-0003-4364-9769;-
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Item type: Article ID code: 76529 Dates: DateEvent15 March 2022Published25 May 2021Published Online22 May 2021AcceptedNotes: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Advanced Manufacturing and MaterialsDepositing user: Pure Administrator Date deposited: 24 May 2021 09:41 Last modified: 27 Sep 2024 01:21 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/76529