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A platform for research: civil engineering, architecture and urbanism
Unmanned aircraft path planning for construction safety inspections
Abstract With its flexible and convenient features, the Unmanned Aircraft System (UAS) provides an efficient platform for safety inspections in hazardous areas. Yet sparse studies have explored path planning for construction safety inspections. This research developed a multi-precision unmanned aircraft path planning system (MP-UAS) by using MATLAB for safety inspections on partially known 3D exterior construction environments. Firstly, a detailed house-building checklist and monitoring levels are formulated. Subsequently, based on the principle of merging pixel points in four neighborhoods, the Four-neighborhood Growing algorithm extracts information from the point cloud grid model to divide inspection areas. Finally, the Ant Colony Optimization (ACO) combined with four-element optimization achieves a full coverage optimal design of UAS path in two-dimensional planes above the inspection area. The system greatly reduces the path length and minimizes energy consumption while meeting requirements of coverage accuracy in the validation case, and is expected to facilitate safety inspection and decision-making.
Highlights MP-UAS was developed for construction safety inspection. UAS monitoring level standards based on the characteristics of house-building construction projects was established. Four-neighborhood Growing Algorithm segmented the different monitoring level areas. ACO combined with 4-opt method to optimize the UAS flight path. MP-UAS promoted the development of UAS-assisted construction site refined management.
Unmanned aircraft path planning for construction safety inspections
Abstract With its flexible and convenient features, the Unmanned Aircraft System (UAS) provides an efficient platform for safety inspections in hazardous areas. Yet sparse studies have explored path planning for construction safety inspections. This research developed a multi-precision unmanned aircraft path planning system (MP-UAS) by using MATLAB for safety inspections on partially known 3D exterior construction environments. Firstly, a detailed house-building checklist and monitoring levels are formulated. Subsequently, based on the principle of merging pixel points in four neighborhoods, the Four-neighborhood Growing algorithm extracts information from the point cloud grid model to divide inspection areas. Finally, the Ant Colony Optimization (ACO) combined with four-element optimization achieves a full coverage optimal design of UAS path in two-dimensional planes above the inspection area. The system greatly reduces the path length and minimizes energy consumption while meeting requirements of coverage accuracy in the validation case, and is expected to facilitate safety inspection and decision-making.
Highlights MP-UAS was developed for construction safety inspection. UAS monitoring level standards based on the characteristics of house-building construction projects was established. Four-neighborhood Growing Algorithm segmented the different monitoring level areas. ACO combined with 4-opt method to optimize the UAS flight path. MP-UAS promoted the development of UAS-assisted construction site refined management.
Unmanned aircraft path planning for construction safety inspections
Yu, Liangcheng (author) / Huang, Merit M. (author) / Jiang, Suwen (author) / Wang, Chen (author) / Wu, Mabao (author)
2023-06-29
Article (Journal)
Electronic Resource
English
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