QCar

Learning Outcomes

The QCar provides a unique platform for exploring advanced self-driving technologies and validating research concepts. Through hands-on experiences, students will:

• Develop hands-on experience with machine learning and AI applications.
• Gain expertise in vehicle navigation, mapping, and path planning using LIDAR and vision systems.
• Work with dataset generation for computer vision applications and autonomous control.
• Explore advanced concepts in multi-agent and swarm robotics.
• Design, simulate, and implement control algorithms using Simulink®, Python™, ROS, and C/C++.

QCar Details

The Qcar comes fully equipped for academic research and includes:

• NVIDIA® Jetson™ TX2 supercomputer for high-speed processing.
• LIDAR with 2K-8K resolution, 12m range, and 10-15Hz scan rate.
• Intel D435 RGBD camera for 360° vision and depth perception.
• Built-in 9-axis IMU, encoders, and dual microphones for environmental sensing.
• User-expandable I/O, including SPI, I2C, GPIO, and multiple USB ports for custom applications.
• Safety features, such as a hardware “safe” shutdown button and auto-power off for battery protection.
• Compatibility with industry-leading software tools, including Simulink®, ROS, and TensorFlow®.
• Dimensions: 39 x 19 x 20 cm
• Weight: 2.7 kg (with batteries)
• Battery life: 30 minutes of driving or 2 hours stationary (with sensor feedback)
• Sensors: LIDAR, 360° RGBD camera, encoders, IMU, microphones
• Connectivity: WiFi 802.11 a/b/g/n/ac with dual antennas, Ethernet, HDMI ports
• Power: 3S 11.1V LiPo battery with onboard power management 

 

 

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