Multi-UAV trajectory planning for 3D visual inspection of complex structures

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

doi:10.1016/j.autcon.2022.104709

Cited by 26 publications

(11 citation statements)

References 37 publications

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“…Besides, Peng et al [52] proposed a CPP method for covering large-scale 3D urban constructions. Ivić et al [53] proposed a Heat Equation Driven Area Coverage (HEDAC) algorithm to address the 3D inspection problem of complex structures. These methods ensured that every point of the target could be inspected via at least one point on the paths.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Enhancement of Resolution-Aware Coverage Path Planning Method for Surface Inspection Using Unmanned Aerial Vehicles

Wu,

Funabora,

Doki

et al. 2024

IEEE Access

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We implemented and evaluated our previous path planning method for inspection using unmanned aerial vehicles (UAVs) in real-world, and identified its shortcomings in handling positioning errors. Then, we proposed an enhanced method to address this problem. The previous method theoretically guaranteed complete coverage of targets and data quality. However, we verified it in bridge inspection experiments and found that the former has not been ensured. The crucial factors of data omission are clarified as the errors in UAV positioning. Our previous method relies on appropriately setting ideal allowances to counteract positioning errors, which is impractical. Therefore, we proposed an enhanced path planning method, which adaptively adjusts allowances according to positioning error to prevent omission while minimizing waypoints. In the simulation including positioning disturbances, the enhanced method consistently achieved full coverage in 1000 times simulation with over 28% waypoints less than the previous one.

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Section: Related Workmentioning

confidence: 99%

Evaluation and Enhancement of Resolution-Aware Coverage Path Planning Method for Surface Inspection Using Unmanned Aerial Vehicles

Wu,

Funabora,

Doki

et al. 2024

IEEE Access

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“…Communication cooperative control plays a key role in multiple UAV systems, ensuring cooperative obstacle avoidance between UAVs through cluster coordination and centralized control decisions [75]. In contrast, predictive control strategies avoid potential collision threats in advance by predicting environmental changes and adopting predictive path planning [93]. The integrated application of these control strategies ensures that UAV systems can intelligently identify and avoid potential collision risks in flight, improving flight safety and reliability.…”

Section: Collision Detectionmentioning

confidence: 99%

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Wang,

Huang,

et al. 2024

Processes

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In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology.

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“…All these are being assisted by various technological features including machine learning, deep learning, and artificial intelligence [18,26,27]. Trajectory planning of UAVs has attracted the attention of researchers in many ways, both in single and multiple UAV systems, because with a selection of the most appropriate route a lot of energy can be saved [19,20,[28][29][30][31]. Therefore, to make the process of real monitoring of cultivation fields very efficient, the cost has to be optimized and hence proper trajectory planning is extremely necessary for the operations of UAV systems.…”

Section: Related Workmentioning

confidence: 99%

“…Category (ii) can be further classified into three subcategories based on specialized techniques that are applied to plan flight paths of UAVs: (a). Ant-colony-based optimization [29][30][31][32][33][34][35][36][37][38][39][40][41]. (b).…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

Al-Naeem

Rahman

Banerjee³

et al. 2023

Sustainability

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Crop monitoring and smart spraying have become indispensable parts of precision agriculture where unmanned aerial vehicles (UAVs) play a lead role. In particular, in large agricultural fields, aerial monitoring is a sustainable solution provided it can be performed in an energy-efficient manner. The existing literature points out that the research on precision agriculture using UAVs is still very minimal. In this article, we propose a support vector machine (SVM)-based UAV location management technique where UAVs change position over various portions or regions of a large agricultural field so that crops are properly monitored in an energy-efficient manner. Whenever a processing request is generated from any sensor in a part of the field, the UAV investigates with an SVM to decide whether to move on to the center of that field based on various parameters or characteristics such as region-id, packet-id, time of day, waiting times of the packets, the average waiting time of others within a predefined time window, location of the UAV, residual energy of the UAV after processing the packet, and movement after processing the packet. We use 70% of our data for training and the other 30% for testing. In our simulation study, we use accuracy, precision, and recall to measure in both contexts to determine the efficiency of the model, and also the amount of energy preserved is computed corresponding to every move. We also compare our approach with current state-of-the-art energy-preserving UAV movement control techniques which are compatible with the present application scenario. The proposed technique produced 6.5%, 34.5%, and 61.5% better results in terms of percentage of successful detection (PSD), composite energy consumption (CEC), and average delay (ADL), respectively.

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Multi-UAV trajectory planning for 3D visual inspection of complex structures

Cited by 26 publications

References 37 publications

Evaluation and Enhancement of Resolution-Aware Coverage Path Planning Method for Surface Inspection Using Unmanned Aerial Vehicles

Evaluation and Enhancement of Resolution-Aware Coverage Path Planning Method for Surface Inspection Using Unmanned Aerial Vehicles

A Review of Collaborative Trajectory Planning for Multiple Unmanned Aerial Vehicles

Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands

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