首页|Findings from China Three Gorges University Yields New Data on Robotics (Mobile Robot Path Planning With Two Stages Based On Hybrid Intelligent Optimisation Algorithm)

Findings from China Three Gorges University Yields New Data on Robotics (Mobile Robot Path Planning With Two Stages Based On Hybrid Intelligent Optimisation Algorithm)

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A new study on Robotics is now available. According to news reporting from Yichang, People's Republic of China, by NewsRx journalists, research stated, "In this paper, a hybrid intelligent optimisation (HIO) algorithm is presented to solve the path planning problem of the mobile robot with two stages. At the first stage, the Dijkstra algorithm is used to determine the dimension of the element in the population and the current optimal solution for MRPP." The news correspondents obtained a quote from the research from China Three Gorges University, "At the second stage, a new screening mechanism is proposed, where the whole population is divided into three groups, i.e., the top element group, the middle element group, and the low element group by using the improved five -elements cycle model (IFECM). Then the PSO algorithm and the crossover operator are used to update the elements in the middle element group. The mutation operator is used to update the elements in the low element group. The updated middle element group, low element group are used to update the top element group and gbest. Finally, the key point deletion and turning point optimisation processing are implemented, which contribute to generating a flatter path and avoiding obstacles." According to the news reporters, the research concluded: "Compared with 14 other algorithms, simulation experiments on mobile robot path planning under eight different scenarios prove that the proposed method achieves the highest success rate in planning paths and the shortest generated paths." This research has been peer-reviewed.

YichangPeople's Republic of ChinaAsiaAlgorithmsEmerging TechnologiesMachine LearningRobotRoboticsChina Three Gorges University

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.29)