LineRanger: Analysis and Field Testing of an Innovative Robot for Efficient Assessment of Bundled High-Voltage Powerlines

Richard, Pierre-Luc; Pouliot, Nicolas; Morin, François; Lepage, Marco; Hamelin, Philippe; Lagacc, M.; Sartor, Alex; Lambert, Ghislain; Montambault, Serge

doi:10.1109/icra.2019.8794397

Cited by 33 publications

(9 citation statements)

References 10 publications

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“…And a good one can greatly improve the efficiency. For example, compared to LineScout [115], LineRanger [116] takes much less time to cross obstacles because of the ingenious and passive obstacle-crossing mechanism. However, the mechanical design of PTL inspection robots is not refined now, and robots cannot balance inspection and operation.…”

Section: Prospectsmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

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Section: Prospectsmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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“…See (6) for the calculation method. The x-axis of the ROC curve is (1 − speci f icity), so the final formula can be simplified to Label (7):…”

Section: Calculation Formula For Classifier Evaluationmentioning

confidence: 99%

“…Recently, transmission lines have been built with bundled conductors because of the increasing power demand. However, conventional robots can only inspect a line while traveling along it [7]. Thus, most research focused on developing new robot architectures for smart navigation over bundle transmission lines [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

et al. 2019

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The global demand for electric power has been greatly increasing because of industrial development and the change in people’s daily life. A lot of overhead transmission lines have been installed to provide reliable power across long distancess. Therefore, research on overhead transmission lines inspection is very important for preventing sudden wide-area outages. In this paper, we propose an Overhead Transmission Line Classifier (OTL-Classifier) based on deep learning techniques to classify images returned by future unmanned maintenance drones or robots. In the proposed model, a binary classifier based on Inception architecture is incorporated with an auxiliary marker algorithm based on ResNet and Faster-RCNN(Faster Regions with Convolutional Neural Networks features). The binary classifier defines images with foreign objects such as balloons and kites as abnormal class, regardless the type, size, and number of the foreign objects in a single image. The auxiliary marker algorithm marks foreign objects in abnormal images, in order to provide additional help for quick location of hidden foreign objects. Our OTL-Classifier model achieves a recall rate of 95% and an error rate of 10.7% in the normal mode, and a recall rate of 100% and an error rate of 35.9% in the Warning–Review mode.

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“…Depending on the application some of these robotic use cases may involve the robot actively manipulating objects, whereas other applications may be simply observational-getting where it is too dangerous or constrained for humans to be. Recent inspection examples include: bridges [2], HVAC inspection [3], powerlines [4], and even wildlife [5].…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment for Concrete Sewer Pipes Using Displacement Probes: A Robotic Design Case Study

Ross

Stumpf

Barnett³

et al. 2021

Robotics

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Worldwide, millions of kilometres of sewers are constructed from concrete pipes. Unfortunately, concrete sewers are susceptible to corrosion from biogenic hydrogen sulphide, and, though they may pass visual inspection, their ability to hold together under load may be degraded. This paper presents the design of a teleoperated robot with a protractible probe, that allows an operator to apply a localised load to selected points within a concrete sewer pipe. We report findings from laboratory and field trials of our prototype, with initial results suggesting that this approach has the potential to contribute useful information to sewer maintenance planning.

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LineRanger: Analysis and Field Testing of an Innovative Robot for Efficient Assessment of Bundled High-Voltage Powerlines

Cited by 33 publications

References 10 publications

Environment Perception Technologies for Power Transmission Line Inspection Robots

Environment Perception Technologies for Power Transmission Line Inspection Robots

OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance

Condition Assessment for Concrete Sewer Pipes Using Displacement Probes: A Robotic Design Case Study

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