Environment Understanding Algorithm for Substation Inspection Robot Based on Improved DeepLab V3+

Wang, Ping; Li, Chuanxue; Yang, Qiang; Lin, Fu‐Cheng; Yu, Fan; Min, Lixiao; Guo, Dequan; Li, Xinming

doi:10.3390/jimaging8100257

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…The amount of time it takes for an agent to react to an inquiry considers the amount of time The ratio of corrected occurrences to total occurrences was used to determine accuracy. Existing techniques, such as improved adaptive control algorithm-feedforward neural network (IACA-FNN) (Khan et al, 2021), enhanced unified algorithm-susceptible-infectedremoved (EUA-SIR) (Wang, Li, et al, 2022), and bounded behaviorparticle swarm optimization (BB-PSO) (Zhang, Xie, et al, 2021), are compared with our proposed PID-ERDA method.…”

Section: Resultsmentioning

confidence: 99%

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Tang,

Xue,

et al. 2024

Journal of Field Robotics

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In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

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Section: Resultsmentioning

confidence: 99%

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Tang,

Xue,

et al. 2024

Journal of Field Robotics

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“…By processing and analyzing visual information such as images and videos, the machine can simulate human visual ability and make corresponding decisions and actions. Machine vision technology plays an important role in substation inspection robot [9][10]. Firstly, machine vision can be used for environment perception and target detection.…”

Section: Application Of Machine Vision In Inspection Robotmentioning

confidence: 99%

Navigation of Substation Inspection Robot Based on Machine Vision

Fan,

Zhu,

Chen

et al. 2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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The research on robot navigation of substation inspection based on machine vision has important background significance. The purpose of this study is to evaluate and optimize the application effect of machine vision in the navigation of substation inspection robot. The experimental data show that the navigation system based on machine vision achieves 92.5% navigation accuracy and 85.2% navigation accuracy in environment A, which provides strong support for improving the efficiency and safety of substation inspection. In addition, it is found that the navigation accuracy drops to 76.4% and 64.9% in the case of sensor failure, so it is necessary to further improve the sensor system. Machine vision navigation technology has great potential in substation inspection robot, which is worthy of in-depth research and application.

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“…This is significant considering that U-Net is primarily applied to issue 1, February 2024 small-scale medical images [9], [10]. Both U-Net [11]- [14] and DeepLabV3+ [15]- [18] have undergone numerous modifications to improve performance and adapt to hardware conditions with limited computational resources [19].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Segmentation of Multispectral Images Using U-Net and DeeplabV3+ Architecture

Herlawati,

Handayanto

2024

Jurnal Ilmu Komputer dan Informasi

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The application of Deep Learning has now extended to various fields, including land cover classification. Land cover classification is highly beneficial for urban planning. However, the current methods heavily rely on statistical-based applications, and generating land cover classifications requires advanced skills due to their manual nature. It takes several hours to produce a classification for a province-level area. Therefore, this research proposes the application of semantic segmentation using Deep Learning techniques, specifically U-Net and DeepLabV3+, to achieve fast land cover segmentation. This research utilizes two scenarios, namely scenario 1 with three land classes, including urban, vegetation, and water, and scenario 2 with five land classes, including agriculture, wetland, urban, forest, and water. Experimental results demonstrate that DeepLabV3+ outperforms U-Net in terms of both speed and accuracy. As a test case, Landsat satellite images were used for the Karawang and Bekasi Regency areas.

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Environment Understanding Algorithm for Substation Inspection Robot Based on Improved DeepLab V3+

Cited by 5 publications

References 33 publications

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Implementation of PID controller and enhanced red deer algorithm in optimal path planning of substation inspection robots

Navigation of Substation Inspection Robot Based on Machine Vision

Land Cover Segmentation of Multispectral Images Using U-Net and DeeplabV3+ Architecture

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