International Journal on Science and Technology

E-ISSN: 2229-7677     Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 1 January-March 2026 Submit your research before last 3 days of March to publish your research paper in the issue of January-March.

AI-Driven Virtual Robotic Arm Simulation via OpenCV and Inverse Kinematics

Author(s) Mr. ANAS IFTEKHAR BEPARI, Dr. SHIVANGI VIRAL THAKKER
Country India
Abstract This study presents the design and implementation of an AI-driven virtual robotic arm simulation that integrates computer vision and inverse kinematics (IK) for real-time motion control and visualization. The proposed system, developed using Python, OpenCV, and Pygame, enables motion tracking and arm manipulation within a fully software-based framework, eliminating the need for physical sensors or hardware. A webcam captures the real-time video stream, which undergoes HSV-based color segmentation and contour analysis to identify target markers. These markers are processed through inverse kinematic equations to compute joint angles and simulate accurate arm motion. Unlike conventional robotic platforms requiring costly equipment and calibration, this virtual system operates efficiently on standard computing hardware while maintaining high positional accuracy and smooth response at 25–30 fps.
Experimental validation demonstrates that the system achieves an average positional deviation of ±2 pixels, corresponding to sub-centimeter precision within the simulation workspace. The integrated AI-driven vision framework ensures adaptability to varying lighting and background conditions. The system’s modular architecture allows parameter customization for joint limits, link lengths, and target detection thresholds, making it highly suitable for educational, industrial training, and research environments. The results confirm that vision-based inverse kinematic models can effectively replicate the dynamic behavior of physical manipulators, establishing a scalable foundation for 3D simulation, gesture-based teleoperation, and autonomous robotic control.
Keywords Computer Vision, Inverse Kinematics, OpenCV, AI-Controlled Manipulators, Human–Robot Interaction
Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-03-26

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