Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Salahat, Ehab; Asselineau, Charles-Alexis; Coventry, Joe; Mahony, Robert

doi:10.1109/iecon.2019.8927123

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Finally, the results obtained with the BECD, and GBWC methods differ from the results obtained by other authors [34][35][36][37]. Who did not implement CPPs to solve these types of problems; they used other types of solutions that have restrictions when inspecting PV plants with UAVs.…”

Section: Discussionmentioning

confidence: 73%

“…Irrespective of these approaches, an important aspect is that the UAV should automatically follow the path to cover important points in PV plants. Many research efforts have been made to calculate the paths of and solve the waypoint planning problem for UAV inspection of solar PV plant applications [34][35][36][37], but none of them propose the coverage path planning (CPP) as a method to complete this task.…”

Section: Introductionmentioning

confidence: 99%

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Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

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Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

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Section: Discussionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

et al. 2021

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“…In outdoor operations, like photovoltaic panel inspections, pilots predefine a series of GPS waypoints for the UAV [17]. Following these waypoints, the UAV captures images of the plant at specified intervals.…”

Section: Related Work 21 Autonomous Inspection Of Industrial Faciliti...mentioning

confidence: 99%

Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs

Rodriguez-Vazquez,

Prieto-Centeno,

Fernandez-Cortizas

et al. 2024

Sensors

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Robotic missions for solar farm inspection demand agile and precise object detection strategies. This paper introduces an innovative keypoint-based object detection framework specifically designed for real-time solar farm inspections with UAVs. Moving away from conventional bounding box or segmentation methods, our technique focuses on detecting the vertices of solar panels, which provides a richer granularity than traditional approaches. Drawing inspiration from CenterNet, our architecture is optimized for embedded platforms like the NVIDIA AGX Jetson Orin, achieving close to 60 FPS at a resolution of 1024 ×1376 pixels, thus outperforming the camera’s operational frequency. Such a real-time capability is essential for efficient robotic operations in time-critical industrial asset inspection environments. The design of our model emphasizes reduced computational demand, positioning it as a practical solution for real-world deployment. Additionally, the integration of active learning strategies promises a considerable reduction in annotation efforts and strengthens the model’s operational feasibility. In summary, our research emphasizes the advantages of keypoint-based object detection, offering a practical and effective approach for real-time solar farm inspections with UAVs.

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“…Studies aiming at optimizing path planning include different approaches to the problem. In the study developed by Salahat et al [52], the traveling salesman shortest path algorithm was used to generate a path that includes a randomly selected set of modules that represent the entire PV plant, allowing an optimization of the battery use. Ding et al [53] based their method on density clustering, boustrophedon path planning and Bezier curves.…”

Section: Automatic Path Planningmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

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Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Cited by 10 publications

References 14 publications

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Coverage Path Planning with Semantic Segmentation for UAV in PV Plants

Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

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