Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model
The rapid growth in the use of wind energy has led to significant challenges in the inspection and maintenance of wind turbine blades, especially as turbine sizes increase dramatically and as operational environments become harsh and unpredictable. Wind turbine blades, being the most expensive and f...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/2075-1702/13/3/180 |
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| author | Da Chen Gang Yu Shuchen Huang |
| author_facet | Da Chen Gang Yu Shuchen Huang |
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| collection | DOAJ |
| description | The rapid growth in the use of wind energy has led to significant challenges in the inspection and maintenance of wind turbine blades, especially as turbine sizes increase dramatically and as operational environments become harsh and unpredictable. Wind turbine blades, being the most expensive and failure-prone components, directly affect operational stability and energy efficiency. The efficient and precise inspection of these blades is therefore essential to ensuring the sustainability and reliability of wind energy production. To overcome the limitations of the existing inspection methods, which suffer from low detection precision and inefficiency, this paper proposes a novel complete coverage path planning (CCPP) algorithm for wall-climbing robots operating on wind turbine blades. The proposed algorithm specifically targets highly complex regions with significant curvature variations, utilizing 3D point cloud data to extract height information for the construction of a 2.5D grid map. By developing a tailored energy consumption model based on diverse robot motion modes, the algorithm is integrated with a bio-inspired neural network (BINN) to ensure optimal energy efficiency. Through extensive simulations, we demonstrate that our approach outperforms the traditional BINN algorithms, achieving significantly superior efficiency and reduced energy consumption. Finally, experiments conducted on both a robot prototype and a wind turbine blade platform validate the algorithm’s practicality and effectiveness, showcasing its potential for real-world applications in large-scale wind turbine inspection. |
| format | Article |
| id | doaj-art-2672cf2c11a045f2bb49a23a43da351e |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-2672cf2c11a045f2bb49a23a43da351e2025-08-20T02:42:26ZengMDPI AGMachines2075-17022025-02-0113318010.3390/machines13030180Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption ModelDa Chen0Gang Yu1Shuchen Huang2School of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, ChinaThe rapid growth in the use of wind energy has led to significant challenges in the inspection and maintenance of wind turbine blades, especially as turbine sizes increase dramatically and as operational environments become harsh and unpredictable. Wind turbine blades, being the most expensive and failure-prone components, directly affect operational stability and energy efficiency. The efficient and precise inspection of these blades is therefore essential to ensuring the sustainability and reliability of wind energy production. To overcome the limitations of the existing inspection methods, which suffer from low detection precision and inefficiency, this paper proposes a novel complete coverage path planning (CCPP) algorithm for wall-climbing robots operating on wind turbine blades. The proposed algorithm specifically targets highly complex regions with significant curvature variations, utilizing 3D point cloud data to extract height information for the construction of a 2.5D grid map. By developing a tailored energy consumption model based on diverse robot motion modes, the algorithm is integrated with a bio-inspired neural network (BINN) to ensure optimal energy efficiency. Through extensive simulations, we demonstrate that our approach outperforms the traditional BINN algorithms, achieving significantly superior efficiency and reduced energy consumption. Finally, experiments conducted on both a robot prototype and a wind turbine blade platform validate the algorithm’s practicality and effectiveness, showcasing its potential for real-world applications in large-scale wind turbine inspection.https://www.mdpi.com/2075-1702/13/3/180complete coverage path planningwall-climbing robotwind turbine bladebio-inspired neural networkenergy consumption model2.5D grid map |
| spellingShingle | Da Chen Gang Yu Shuchen Huang Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model Machines complete coverage path planning wall-climbing robot wind turbine blade bio-inspired neural network energy consumption model 2.5D grid map |
| title | Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model |
| title_full | Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model |
| title_fullStr | Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model |
| title_full_unstemmed | Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model |
| title_short | Complete Coverage Path Planning for Wind Turbine Blade Wall-Climbing Robots Based on Bio-Inspired Neural Networks and Energy Consumption Model |
| title_sort | complete coverage path planning for wind turbine blade wall climbing robots based on bio inspired neural networks and energy consumption model |
| topic | complete coverage path planning wall-climbing robot wind turbine blade bio-inspired neural network energy consumption model 2.5D grid map |
| url | https://www.mdpi.com/2075-1702/13/3/180 |
| work_keys_str_mv | AT dachen completecoveragepathplanningforwindturbinebladewallclimbingrobotsbasedonbioinspiredneuralnetworksandenergyconsumptionmodel AT gangyu completecoveragepathplanningforwindturbinebladewallclimbingrobotsbasedonbioinspiredneuralnetworksandenergyconsumptionmodel AT shuchenhuang completecoveragepathplanningforwindturbinebladewallclimbingrobotsbasedonbioinspiredneuralnetworksandenergyconsumptionmodel |