Robotics & Machine Learning Daily News2024,Issue(Oct.4) :151-151.

Study Findings on Robotics Are Outlined in Reports from Northeast Forestry Unive rsity (Path Planning for Wall-Climbing Robots Using an Improved Sparrow Search A lgorithm)

Robotics & Machine Learning Daily News2024,Issue(Oct.4) :151-151.

Study Findings on Robotics Are Outlined in Reports from Northeast Forestry Unive rsity (Path Planning for Wall-Climbing Robots Using an Improved Sparrow Search A lgorithm)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on robotics have been published . According to news reporting from Harbin, People's Republic of China, by NewsRx journalists, research stated, "Traditional path planning algorithms typically f ocus only on path length, which fails to meet the low energy consumption require ments for wall-climbing robots in bridge inspection." Financial supporters for this research include Fundamental Research Funds For Th e Central Universities. The news correspondents obtained a quote from the research from Northeast Forest ry University: "This paper proposes an improved sparrow search algorithm based o n logistic-tent chaotic mapping and differential evolution, aimed at addressing the issue of the sparrow search algorithm's tendency to fall into local optima, thereby optimizing path planning for bridge inspection. First, the initial popul ation is optimized using logistic-tent chaotic mapping and refracted opposition- based learning, with dynamic adjustments to the population size during the itera tive process. Second, improvements are made to the position updating formulas of both discoverers and followers. Finally, the differential evolution algorithm i s introduced to enhance the global search capability of the algorithm, thereby r educing the robot's energy consumption."

Key words

Northeast Forestry University/Harbin/P eople's Republic of China/Asia/Algorithms/Differential Evolution/Emerging Te chnologies/Machine Learning/Nano-robot/Robotics/Search Algorithms

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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