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<p>To address the unstable target localization and insufficient path planning efficiency caused by complex lighting and dynamic environments during automated robotic arm loading, this study proposes a method for automated robotic arm loading that integrates visual guidance and an improved sparrow search algorithm. This method constructs a visual guidance system based on a depth camera, achieving reliable target object recognition and 3D pose mapping through template matching and depth information fusion, providing stable spatial perception information for robotic arm grasping. Furthermore, non-dominated sorting and multinomial mutation mechanisms are introduced to improve the traditional sparrow search algorithm, establishing a trajectory planning model with motion time and impact smoothness as optimization objectives, thereby enhancing the algorithm’s optimization ability under multi-objective constraints and dynamic environments. Experimental results show that in typical industrial scenarios, this method achieves a high grasping success rate and outperforms comparative methods in positioning accuracy and path planning efficiency, validating the effectiveness and practicality of the proposed method in automated robotic arm loading tasks.</p>

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Mechanical fault diagnosis method based on multi-strategy improved SSA

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月6日

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  • Xiangtao Kong

摘要

To address the unstable target localization and insufficient path planning efficiency caused by complex lighting and dynamic environments during automated robotic arm loading, this study proposes a method for automated robotic arm loading that integrates visual guidance and an improved sparrow search algorithm. This method constructs a visual guidance system based on a depth camera, achieving reliable target object recognition and 3D pose mapping through template matching and depth information fusion, providing stable spatial perception information for robotic arm grasping. Furthermore, non-dominated sorting and multinomial mutation mechanisms are introduced to improve the traditional sparrow search algorithm, establishing a trajectory planning model with motion time and impact smoothness as optimization objectives, thereby enhancing the algorithm’s optimization ability under multi-objective constraints and dynamic environments. Experimental results show that in typical industrial scenarios, this method achieves a high grasping success rate and outperforms comparative methods in positioning accuracy and path planning efficiency, validating the effectiveness and practicality of the proposed method in automated robotic arm loading tasks.