Robotics & Machine Learning Daily News2024,Issue(Feb.13) :75-76.DOI:10.1016/j.jestch.2023.101610

Researchers at Sungkyunkwan University Publish New Data on Robotics (Unloading sequence planning for autonomous robotic container-unloading system using A-star search algorithm)

Robotics & Machine Learning Daily News2024,Issue(Feb.13) :75-76.DOI:10.1016/j.jestch.2023.101610

Researchers at Sungkyunkwan University Publish New Data on Robotics (Unloading sequence planning for autonomous robotic container-unloading system using A-star search algorithm)

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Abstract

South Korea, by NewsRx correspondents, research stated, “In autonomous unloading systems, one of the major challenges is planning the unloading sequence in cluttered logistics container environments.” Financial supporters for this research include Ministry of Trade, Industry And Energy; Ministry of Science, Ict And Future Planning; Korea Ministry of Science And Ict. Our news correspondents obtained a quote from the research from Sungkyunkwan University: “Proper unloading sequence planning is crucial to avoid damage to the packages caused by collision and collapse. At the same time, the planned sequence has to minimize the effort during the unloading process, such as energy consumption, time taken, and distance moved, to enhance efficiency. This paper presents a sequence planning method based on the A-star search algorithm applied to unloading randomly stacked unidentical boxes from logistics containers. To represent the general overlap relationships of boxes in a cluttered environment, we utilize an adjacency matrix structure and involve it in the state of the unloading sequence planning. Moreover, by adopting the A-star search algorithm, the method can minimize the cost of system movement for an efficient unloading sequence. As a result, our method can determine the unloading sequence while considering the relationships between packages.”

Key words

Sungkyunkwan University/Suwon/South Korea/Asia/Algorithms/Autonomous Robot/Emerging Technologies/Machine Learning/Robotics/Robots/Search Algorithms

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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被引量2
参考文献量24
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