Dynamic grasping system based on visual algorithm and robot arm collaboration in logistics production line.

Abstract

In response to the urgent need for efficient and accurate dynamic grasping in automated logistics, this study proposes the VRCDS, a dynamic grasping system that integrates a multi-feature weighted PnP vision algorithm with multi-robot arm collaboration. The system establishes a closed-loop "perception-decision-execution-feedback" architecture, significantly enhancing grasping accuracy, system efficiency, and energy savings compared to traditional single-arm or visual servo schemes. Experimental results demonstrate that the VRCDS system achieves high recognition accuracy, robust grasping efficiency under various conveyor speeds, precise trajectory control, and a substantial reduction in power consumption. The research provides an efficient, precise, and reliable solution for dynamic grasping tasks in logistics automation.