Multi-Uav Trajectory Planning for 3d Visual Inspection of Complex Structures

Ivić, Stefan; Crnković, Bojan; Grbčić, Luka; Matleković, Lea

doi:10.2139/ssrn.4096560

Cited by 3 publications

(3 citation statements)

References 37 publications

(4 reference statements)

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“…This requires mission planning which can be either manually defined or automatically derived by the bridge's CAD data or point cloud data in combination with set parameters including sensor and lens specs, minimum or maximum working distance, and the maximum camera angle to the bridge surface. Different approaches and concepts for automated inspection using UAVs (Angel Ortega et al, 2020); (Bolourian and Hammad, 2020); (Bono et al, 2022); (Chen et al, 2019); (Ivić et al, 2022); (Shi, Mehrooz and Jacobsen, 2021) and UGVs (Charron et al, 2001); (Merkle, Schmitt and Reiterer, 2020a); (Peel et al, 2018) already exist. Bridges usually require simultaneous localization and mapping (SLAM) due to missing Global Navigation Satellite System (GNSS) below a bridge either based on cameras or Light Detection and Ranging (LiDAR) for navigation.…”

Section: Conceptual Methodology For An Automated Crack Inspection In ...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

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Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.

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Section: Conceptual Methodology For An Automated Crack Inspection In ...mentioning

confidence: 99%

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Merkle,

Solass,

Schmitt

et al. 2023

ITcon

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“…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. Implementing the distance field and adjusting the agent's directional vector when the distance threshold is crossed prevents collisions between the agents and the domain borders 64 . It is also possible to use this technique in 3D settings.…”

Section: Characteristics Of Coverage Path Planning For Uavmentioning

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

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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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“…From the present point of view, path planning algorithms are favored by many researchers with the rise of mobile robots, UAVs [7][8][9], the unmanned field [10,11], and the video game field [12]. However, the study of path planning for mobile robots, especially the trajectory optimization of generated paths, remains a great challenge.…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

Luo

Qin

et al. 2022

Algorithms

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The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61–4.68% and the total turning angle of the path by 58.71–84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59–99.22% compared with the AP-Theta* algorithm.

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Multi-Uav Trajectory Planning for 3d Visual Inspection of Complex Structures

Cited by 3 publications

References 37 publications

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

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

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

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