2024
DOI: 10.3390/app14041637
|View full text |Cite
|
Sign up to set email alerts
|

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

Yanqi Wang,
Xinyan Qin,
Wenxing Jia
et al.

Abstract: 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 mod… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 45 publications
0
2
0
Order By: Relevance
“…In general, the production of a ton of steel requires the consumption of about 0. Multi-objective optimization refers to the research problem involving multiple objective functions, and in the multi-objective optimization mathematical model, each objective function often conflicts with each other, and the improvement of one optimization objective may cause the performance of other optimization objectives to be reduced, which cannot be met at the same time, such as the system applicability maximization objective and the total production cost minimization objective in the power system management optimization problem [36], Uav medical supplies simultaneous pick-up and delivery problems in the operating cost, flight time minimization target and location optimal target [37], objective of total energy consumption minimization and flight time minimization in inspection robot flight problems [38]. Because in the process of carbon reduction technology path research, steel enterprises should not only reduce CO 2 emissions to meet the national low-carbon policy but also ensure the economic benefits of enterprises.…”
Section: Serial Numbermentioning
confidence: 99%
“…In general, the production of a ton of steel requires the consumption of about 0. Multi-objective optimization refers to the research problem involving multiple objective functions, and in the multi-objective optimization mathematical model, each objective function often conflicts with each other, and the improvement of one optimization objective may cause the performance of other optimization objectives to be reduced, which cannot be met at the same time, such as the system applicability maximization objective and the total production cost minimization objective in the power system management optimization problem [36], Uav medical supplies simultaneous pick-up and delivery problems in the operating cost, flight time minimization target and location optimal target [37], objective of total energy consumption minimization and flight time minimization in inspection robot flight problems [38]. Because in the process of carbon reduction technology path research, steel enterprises should not only reduce CO 2 emissions to meet the national low-carbon policy but also ensure the economic benefits of enterprises.…”
Section: Serial Numbermentioning
confidence: 99%
“…Since Deb et al [35] introduced the NSGA-II algorithm, it has become a preferred method for addressing combinatorial optimization challenges, including engineering design, resource allocation, and path planning. Wang et al [36] utilized this algorithm to develop an energy consumption model for the Flying Power Transmission Line Inspection Robot (FPTLIR), targeting its high energy use and lengthy mission durations. Additionally, under dynamic and time-varying conditions, Zou et al [37] crafted a multi-objective optimization model using NSGA-II to refine hazardous chemical transport routes, balancing risk, cost, and carbon emissions.…”
Section: Algorithm Selectionmentioning
confidence: 99%