Design and Development of Vision Based Robotic Arm An Integrated Approach for Object Detection and Autonomous Pick and Place Operations

Vision-guided robotic systems have emerged as a key technology in Industry 4.0 by enabling robots to perceive and interact with dynamic environments. Conventional robotic manipulators typically rely on fixed coordinate programming, limiting their flexibility when object positions change. This paper presents the design and development of a low-cost vision-based robotic arm prototype capable of autonomous object detection and pick-and-place operations. The system integrates a 6-degree-of-freedom (6-DOF) robotic arm fabricated using Fused Deposition Modelling (FDM) 3D printing technology with computer vision techniques based on OpenCV and YOLOv8 object detection. A top-mounted camera captures real-time images of the workspace, and detected object positions are converted from image coordinates to real-world coordinates using homography-based calibration. Inverse kinematics algorithms are employed to compute robotic arm joint movements, while an Arduino UNO microcontroller controls servo actuation through a PCA9685 servo driver. Experimental evaluation demonstrated approximately 92% detection accuracy, coordinate mapping accuracy within ±0.5 cm, and successful autonomous pick-and-place operations under controlled laboratory conditions. The proposed system provides a cost-effective platform for intelligent automation, robotics education, and industrial material handling research.