Method for Motion Route Planning of a Ground Robotic System for Substation Equipment Condition Monitoring and Technical Diagnostics
DOI:
https://doi.org/10.22213/2410-9304-2026-2-90-99Keywords:
ground robotic system, equipment diagnostics, inspection route, base point, 3D model, unity, open switchgearAbstract
This paper presents a method for planning and verifying motion routes of ground robotic systems intended for automated diagnostics of high-voltage substation equipment. The relevance of the study is driven by the need to improve maintenance safety and efficiency under the conditions of severe aging of power infrastructure and increasing requirements for predictive monitoring. The proposed routing system is integrated into a 3D substation model and relies on a combination of geometric, algorithmic, and kinematic methods to generate an optimal trajectory with guaranteed coverage of diagnostic targets. The algorithm comprises sequential stages of site segmentation, discretization of the navigable space, filtering of candidate deployment (base) points according to operational constraints, visibility analysis based on ray tracing and oriented bounding volumes, and optimization of the inspection sequence considering the platformkinematic characteristics. Motion control is implemented using a combination of PID regulation providing stable path following under limited maneuverability and complex spatial layouts typical for substations. The method’s performance is validated through simulation in Unity using a 3D model of a 220 kV substation. The conducted experiments demonstrate full coverage of target objects and stable motion along the planned route. The results confirm the applicability of the proposed solution to automated diagnostics in power facilities and its potential for integration into digital twins of substations.References
Москвин К. В. Расследование причин аварий в электроэнергетике: оценка необходимости изменения и концепция развития нормативного регулирования // Правовой энергетический форум. 2023. № 1. С. 77-88. DOI 10.18254/S231243500025223-3.
Хальясмаа А. И. Машинное обучение как инструмент повышения эффективности управления жизненным циклом высоковольтного электрооборудования // Вестник Иркутского государственного технического университета. 2020. Т. 24, № 5 (154). С. 1093-1104. DOI http://dx.doi.org/10.21285/1814-3520-2020-5-1093-1104.
Romanov A. M., Eroshenko S. A., Gyrichidi N. MAD Robot: Concept and Prototype Description of the Robot for Multi-Spectral Power Equipment Diagnostics. Part I, II // 2023 Belarusian-Ural-Siberian Smart Energy Conference (BUSSEC). P. 136-149.DOI10.1109/ BUSSEC59406.2023.10296467.
Хальясмаа А. И., Петрунько Н. Н. Альтернативный подход к диагностике внешней изоляции высоковольтного оборудования электрических станций и подстанций // Информатика и системы управления. 2024. № 4 (82). С. 72-82. DOI 10.22250/18142400_2024_82_4_72.
Lu S., Zhang Y., Su J. Mobile robot for power substation inspection: A survey. IEEE/CAA Journal of Automatica Sinica.2017. Vol. 4. P. 830-847.DOI:10.1109/JAS.2017.7510364.
Zhang T., Dai J. Electric power intelligent inspection robot: A review.Journal of Physics: Conference Series.2021. Vol. 1750. Article 012023.DOI 10.1088/1742-6596/1750/1/012023.
Dandurand P., Beaudry J., Hebert C., [et al]. All-weather autonomous inspection robot for electrical substations // 2022 IEEE/SICE International Symposium on System Integration (SII). 2022. P. 303-308.DOI: 10.1109/SII52469.2022.9708835.
Zhao W., Cui A., Fang M. [et al]. State assessment of 110-220 kV intelligent substation based on multisensor fusion algorithm control and image vision //Frontiers in Energy Research.2023.Vol. 10. Article 1047359.DOI 10.3389/fenrg.2022.1047359.
Qin X., Wu G., Lei J. [et al]. A Novel Method of Autonomous Inspection for Transmission Line based on Cable Inspection Robot LiDAR Data // Sensors. 2018. Vol. 18. Article. 596.DOI 10.3390/s18020596.
Zheng J., Chen T., He J. [et al.] Review on Security Range Perception Methods and Path-Planning Techniques for Substation Mobile Robots // Energies. 2024. Vol. 17(16). Article. 4106.DOI 10.3390/en17164106.
Luo S., Zhang M., Zhuang Y. A survey of path planning of industrial robots based on rapidly exploring random trees // Frontiers in Neurorobotics. 2023. Vol. 17.DOI 10.3389/fnbot.2023.1268447.
Gyrichidi N., Romanov A. M., Trofimov O. V. [et al.] GNSS-based narrow narrow-angle UV camera targeting: case study of a low-cost mad robot // Sensors. 2024. 24 (11). Article 3494.DOI 10.3390/s24113494.
Unity Real-Time Development Platform. URL: https://unity.com (дата обращения 30.01.2026).
Abdulsaheb J., Kadhim, D. Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey // Robotics.2023. 12 (4).Article 93. DOI 10.3390/robotics12040093.
BiundiniI., Pinto M., Melo A. [et al]. A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection // Sensors.2021., 21 (2). Article 570. DOI 10.3390/s21020570.
Mansouri S., KanellakisC., Fresk E. [et al]. Cooperative coverage path planning for visual inspection // Control Engineering Practice. 2021. 74. 118-131. DOI 10.1016/j.conengprac.2018.03.002.
Williams A., Barrus J. An Efficient and Robust Ray-Box Intersection Algorithm // Journal of Graphics Tools. 2005. Vol. 10, No. 1.DOI:10.1145/1198555. 1198748.
NWH Vehicle Physics 2 Documentation. URL: https://nwhvehiclephysics.com (датаобращения30.01.2026).
Perlin K. An Image Synthesizer // SIGGRAPH.1985. P. 287-296.DOI 10.1145/325165. 325247.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Я В Бренчук, С А Ерошенко, И В Матвеев

This work is licensed under a Creative Commons Attribution 4.0 International License.