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基于深度學(xué)習的多機械手軌跡規劃系統設計
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廣西大學(xué)機械工程學(xué)院

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Design of multi-manipulator trajectory planning system based on deep learning
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    摘要:

    目前提出的多機械手軌跡規劃系統路徑規劃精準度低,避障能力差。基于深度學(xué)習對多機械手的規劃系統進(jìn)行設計,通過(guò)研究傳統系統中存在精確度、智能性不足的缺點(diǎn),在設計的系統分別引入了相應的解決條件,在硬件結構的設計中本文應用ISL-320型號的伺服電機提升多機械手的動(dòng)力功能,應用SKT64系列的芯片提升多機械手的路徑精準度;在應用程序設計上應用擬合算法與疊加算法對規劃路徑中的節點(diǎn)精準運算,在提升系統整體精準度的同時(shí)提升了系統的智能程度。實(shí)驗結果表明,基于深度學(xué)習的多機械手軌跡規劃系統路徑與標準路徑十分接近,說(shuō)明該方法的規劃精準度較高,避障能力得到有效增強。

    Abstract:

    At present, the path planning accuracy of multi manipulator trajectory planning system is low and obstacle avoidance ability is poor. The planning system of multi manipulator is designed based on deep learning. Through the research of the shortcomings of the traditional system, such as the lack of accuracy and intelligence, the corresponding solution conditions are introduced in the designed system. In the design of hardware structure, this paper uses isl-320 servo motor to enhance the power function of multi manipulator, and skt64 series chip is used to improve the path of multi manipulator Accuracy: in the application design, the application of fitting algorithm and superposition algorithm on the nodes in the planning path can improve the overall accuracy of the system and enhance the intelligent degree of the system. The experimental results show that the path of the multi manipulator trajectory planning system based on deep learning is very close to the standard path, which shows that the planning accuracy of the method is high and the obstacle avoidance ability is effectively enhanced.

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盧東來(lái).基于深度學(xué)習的多機械手軌跡規劃系統設計計算機測量與控制[J].,2020,28(11):247-250.

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  • 收稿日期:2020-08-24
  • 最后修改日期:2020-09-18
  • 錄用日期:2020-09-18
  • 在線(xiàn)發(fā)布日期: 2020-11-23
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