首页|Study Findings from Hefei University of Technology Broaden Understanding of Robo tics (Robot Path Planning In Narrow Passages Based On Improved Prm Method)

Study Findings from Hefei University of Technology Broaden Understanding of Robo tics (Robot Path Planning In Narrow Passages Based On Improved Prm Method)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are disc ussed in a new report. According to news reporting originating from Anhui, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Probabilistic roadmap (PRM) method has been shown to perform well in robot path planning. How ever, its performance degrades when the robot needs to pass through narrow passa ges." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from the Hefei University of Technology, "To solve this problem, an improved PRM method with hybrid uniform sampling and Gaussian sampling is proposed in this paper. With the proposed meth od, the robot can improve the success rate and efficiency of path planning in na rrow passages. Firstly, the narrow-passage-aware Gaussian sampling method is dev eloped for narrow passages. Combining uniform sampling globally, the new samplin g strategy can increase the sampling density at the narrow passages and reduce t he redundancy of the samples in the wide-open regions. Then, we propose to use d ensity-based clustering method to achieve accurate identification of narrow chan nels by removing the noise points. Next, graph search algorithm is used to searc h the shortest path from the start point to the goal point. Finally, simulations are carried out to evaluate the validity of the proposed method."

AnhuiPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsHefei University of T echnology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Apr.1)