User libraries and content resources for using Quanser products, including research examples, teaching content, user manuals, guides and more.
Quanser Interactive Labs is a platform that allows users to interface with digital twins of physical lab experiments used for Controls and Robotics courses at thousands of universities around the world.
Autonomous fruit sorting with Quanser QArm 4-DOF + Intel RealSense D415 | Applied Robotics — University of Birmingham
Designed a system that can efficiently sort recyclables and transfer them to corresponding bins using Python, a Raspberry Pi, and Quanser Labs.
Control systems design — PID & lead compensator for a Quanser rotary pendulum (MATLAB/Simulink)
Deep learning perception system in PyTorch — YOLOv8 detection, ENet segmentation (87× parameter reduction), multi-sensor fusion. Real-time edge deployment, sim-to-real analysis.
• Developed an autonomous driving system (QCar2) on NVIDIA Jetson integrating LiDAR, CSI cameras, and Intel RealSense depth sensor for real-time perception • Trained and deployed RT-DETR model for traffic sign detection achieving 92.6% mAP and 24 FPS, enabling future integration into decision-making pipelin
Controlling a high-power quanser robotic arm with a hand-made Galileo Shield
Sending a Qbot a set of conditions via a rasberry pi to sort different bottles in a recycling station
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