FPGA -based vision recognition and robotic arm motion control system

The present study designs and implements a visual recognition and robotic arm control system based on an FPGA (Field-Programmable Gate Array) platform. The system achieves two core functions by operating on a low-cost, single-chip FPGA with limited hardware resources. First, it captures real-time images using a camera. It performs RGB-to-YCbCr color space conversion, morphological filtering, and image segmentation directly on the FPGA to extract the target objects’ color, shape, and position information in real time. Second, it computes the inverse kinematics of the robotic arm by combining the CORDIC algorithm with lookup tables, enabling accurate calculation of joint angles corresponding to target positions and coordinating multi-degree-of-freedom movements. The system has been successfully deployed and tested on an experimental platform, demonstrating its ability to recognize and manipulate multiple target objects, thereby validating its real-time performance and effectiveness in image processing and motion control. Compared with traditional GPU or CPU+FPGA hybrid platforms, the proposed system offers advantages such as faster response speed, lower hardware cost, higher integration, and reconfigurability, making it a promising solution with strong potential for further research and application.