Vision-Based Autonomous Landing of a Hybrid Robot on a Powerline

Li, Zhishuo; Tian, Yunong; Yang, Guodong; Li, En; Zhang, Yanfeng; Chen, Minghao; Liang, Zize; Tan, Min

doi:10.1109/tim.2022.3224524

Cited by 9 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, they require manual assistance to land on and off power lines [16]. Hybrid-mode inspection robots incorporate the combined capabilities of flight and crawling to perform inspections that are durable, energy-saving, safe, and autonomous [17,18]. By switching between flight and crawling modes, the robots can achieve longer endurance and perform more effective inspections.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

Wang,

Qin,

Jia

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In order to improve the flight efficiency of a flying–walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots.

show abstract

Section: Introductionmentioning

confidence: 99%

“…m is the mass of air flow, v in is the induced velocity at point 2, and v o is the induced velocity at point 3. The induced velocity at point 2 can be determined by solving expression (17) as presented as follows.…”

mentioning

confidence: 99%

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

Wang,

Qin,

Jia

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The security of the power system relies on the stable operation of overhead transmission lines [1,2]. Critical components of transmission lines, such as fittings and insulators, which play mechanical and electrical roles in fixing, supporting, connecting, conducting, and insulating, are susceptible to extreme meteorological conditions such as extreme temperatures, frost, and ice.…”

Section: Introductionmentioning

confidence: 99%

An improved cascade RCNN detection method for key components and defects of transmission lines

Dong,

Zhang,

Xie

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

Overhead transmission line detection based on deep learning of aerial images taken by UAVs has been widely investigated. Despite its success, it is limited by several factors, including inappropriate evaluation criteria and dramatic scaling of components in the images. To mitigate these issues, a relative mean Average Precision evaluation index is proposed to accurately measure the model's detection performance for smaller objects. A data enhancement strategy including multi‐scale transformation is adopted to alleviate the problem of drastic scaling. The existing Cascade RCNN target detection technology is enhanced by incorporating Swin‐v2 and a balanced feature pyramid to improve feature characterization capabilities, while side‐aware boundary localization is utilized to improve the positioning accuracy of the model. Experimental results demonstrate that the proposed method outperforms state‐of‐the‐art methods on CPLID and achieves 7.8%, 11.8%, and 5.5% higher detection accuracy than the baseline for mAP50, relative small and medium mAP, respectively. Additionally, the paper discusses the influence of adopted data enhancement on the robustness of the model.

show abstract