Contour-guided zigzag path planning for the automation of pothole spray repair based on the depth image

Wang, Peng-Hui; Xiang, Qingyi; Hu, Yongbiao; Tian, Mingrui

doi:10.1080/10298436.2022.2036339

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…This method evenly tackles surface irregularities while designing the path. Irregularities mean sharp corners, holes, protrusions, concavities, and discontinuities 63 . It identifies and measures the significance of concavities and surface abnormalities and determines whether or not these should be taken into account in the path plan by contrasting the actual part geometry with the convex hull path 55 .…”

Section: Characteristics Of Coverage Path Planning For Uavmentioning

confidence: 99%

“…Irregularities mean sharp corners, holes, protrusions, concavities, and discontinuities. 63 It identifies and measures the significance of concavities and surface abnormalities and determines whether or not these should be taken into account in the path plan by contrasting the actual part geometry with the convex hull path. 55 With a dense population field, the spectra multi-scale coverage algorithm extracts the high-priority parts of the field and guides UAVs to those areas with tremendous potential.…”

Section: Lessons Learned From General Classical Path Planning Techniquesmentioning

confidence: 99%

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Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Mannan

Obaidat

Mahmood

et al. 2022

Int J Communication

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The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.

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Section: Characteristics Of Coverage Path Planning For Uavmentioning

confidence: 99%

Section: Lessons Learned From General Classical Path Planning Techniquesmentioning

confidence: 99%

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Mannan

Obaidat

Mahmood

et al. 2022

Int J Communication

View full text Add to dashboard Cite

show abstract

Pothole Detection Using Image Processing and Data Logging

Giri,

Biradar,

Gupta

et al. 2023

2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

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Weak defect and flatness discrepancy fused identification for robotic measuring of putty repair area

wu,

Yang,

Yang

et al. 2024

Sixteenth International Conference on Digital Image Processing (ICDIP 2024)

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In this study, we introduce a sophisticated automatic identification technique tailored for repair area detection on highspeed train body, with an emphasis on weak defects and flatness discrepancies within putty coatings. The methodology commences with developing a coating weak defect identification strategy, leveraging the Xception network and ASPP module for enhanced accuracy. This is followed by the integration of weak defect identification outcomes into the flatness geometric difference identification framework, effectively reducing aggregated point clouds' interference on flatness assessments. The process culminates in the coating repair areas identification approach, designed to detect defects and necessary machining removals on the coating surface. Our results indicate that by incorporating the effects of defects on flatness into the analysis, the precision of flatness discrepancy detection is significantly improved. Moreover, the amalgamation of defect identification with the evaluation of flatness variations enables more precise mapping of repair zones, representing notable progress in the field of robotic repair technologies.

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Contour-guided zigzag path planning for the automation of pothole spray repair based on the depth image

Cited by 3 publications

References 30 publications

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Pothole Detection Using Image Processing and Data Logging

Weak defect and flatness discrepancy fused identification for robotic measuring of putty repair area

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