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A coverage path planning approach for autonomous radiation mapping with a mobile robot

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dc.citedby1
dc.contributor.authorAbd Rahman N.A.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorHamid N.A.en_US
dc.contributor.authorHou Y.C.en_US
dc.contributor.authorid57855445300en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid57762573900en_US
dc.contributor.authorid37067465000en_US
dc.date.accessioned2023-05-29T09:37:01Z
dc.date.available2023-05-29T09:37:01Z
dc.date.issued2022
dc.descriptionAir navigation; Image segmentation; K-means clustering; Machine design; Mapping; Mobile robots; Motion planning; Radiotherapy; Robot programming; Coverage path planning; Field robotics; Hotspots; Mobile Robot Navigation; Radiation mapping; Radiation maps; Radiation safety; Radiation workers; Regions of interest; Sampling points; Radiationen_US
dc.description.abstractIn nuclear and radiation-related industries, it is crucial to ensure that the radiation dose exposure to the radiation worker is maintained below the permissible dose limit. A radiation map is a useful tool for visualizing the radiation distribution across the work area and for coordinating activities involving the hotspots (high radiation areas). The goal of this work was to design and implement a coverage path planning approach for autonomous radiation mapping carried out by a mobile robot. Given a 2D occupancy map, a method to generate uniformly distributed sampling points was proposed. The geometry of the region of interest, the radiation detector module, and the radiation measurement parameters were considered in formulating the sampling positions. Next, the coverage path planning planner integrates the nearest neighbor and depth-first search algorithms to create a continuous path that enables the robot to visit all the sampling points. The K-means clustering algorithm is added for systematic coverage of a large number of sampling points. The clustering provides options to partition the region of interest into smaller spaces, where the robot would perform the mapping cluster by cluster. Finally, the method of building the radiation map from the acquired data was also presented. The approach was implemented in ROS using a commercial mobile robot equipped with a Geiger�Muller detector. The performance and reliability of the proposed approach were evaluated with a series of simulations and real-world experiments. The results showed that the robot is able to perform autonomous radiation mapping at various target areas. The accuracy of the generated radiation map and the hotspots classifications were also compared and evaluated with conventional manual measurements. Overall, the theoretical frameworks and experiments have provided convincing results in the automation of hazardous work and subsequently toward improving the occupational safety of radiation workers. � The Author(s) 2022.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1177/17298806221116483
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85136478978
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136478978&doi=10.1177%2f17298806221116483&partnerID=40&md5=75d68f23ef5133cf0920dbbf77420939
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26829
dc.identifier.volume19
dc.publisherSAGE Publications Inc.en_US
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Robotic Systems
dc.titleA coverage path planning approach for autonomous radiation mapping with a mobile roboten_US
dc.typeArticleen_US
dspace.entity.typePublication
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