Course Description 

This is a free course that will help you learn about the Kria™ System-on-Module (SOM) and Kria KR260 Robotics Starter Kit, enabling you to accelerate robotics-based applications using the KR260 Starter Kit right out of the box without any installation or FPGA knowledge.

The course also demonstrates how to use the Kria Robotics Stack (KRS) to run prebuilt accelerated robotics applications.

The emphasis of this course is on:

  • Providing an overview of the Kria K26 SOM and its advantages
  • Providing an overview of the Kria KR260 Robotics Starter Kit, its interfaces, and how to get started with the kit
  • Describing the Kria Robotics Stack (KRS) and how it enables roboticists to get up and running in the Robot Operating System (ROS)
  • Running accelerated applications using an Ubuntu image
KR260 Training Videos
1 Getting Started with the Kria KR260 Robotics Starter Kit
Introduces the KR260 Robotics Starter Kit, its features, and interfaces. Explains what the Kria Robotics Stack (KRS) is and how it enables roboticists to get up and running in the Robot Operating System (ROS). Also describes the initial board bring-up process and provides an overview of the accelerated applications that are supported.
2 Launching the ROS 2 Multi-Node Communications via TSN Accelerated Application Using the KR260 Robotics Starter Kit
Covers the basics of what a time sensitive network (TSN) is as well as the TSN IP core. Provides an overview of the TSN application configuration with a base platform definition, out-of-the-box implementation, and real-world usage. 
3 Launching the ROS 2 Perception Node Accelerated Application Using the KR260 Robotics Starter Kit
Provides an overview of the Kria Robotics Stack and the ROS 2 perception node accelerated application and showcases how to implement the perception computational graph using the KR260 Robotics Starter Kit.
4 Launching the 10 Gigabit Ethernet-based Machine Vision Camera Accelerated Application Using the KR260 Robotics Starter Kit
Provides an overview of the Machine Vision (MV) camera application, which will use a Sony IMX547 sensor module to capture images. Also showcases how a 10 Gigabit Ethernet vision pipeline is used to perform defect detection on images of mangos.