Edge AI & Autonomous Navigation
NVIDIA Jetson with CUDA
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Participants can interact with the instructor,
ask questions, seek clarification,
and receive immediate feedback. -
You can receive a full refund before
the second session. After attending
the second session, you will not be eligible
for a refund. -
Access to the session that was originally
conducted in real time. -
You can retake the same course you attended.
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We will provide you with the notes for the course.
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We will provide you with the homework
assignments for additional practice. -
You can always reach out to the instructor
between sessions or after the course on Slack.
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Upcoming Enrollment
November 30th - December 4th
5 sessions 6 hours each
Online, US Eastern Time
Build an autonomous ground robot that maps unknown spaces, spots hazards with on-board GPU vision, and steers around moving obstacles.
Learn how to push your robot to 30+ FPS on NVIDIA Jetson with CUDA, TensorRT, and Python, and prove it with real benchmarks. Go past tutorials and build real autonomy with ROS 2, SLAM, Nav2, and edge AI running on a Pi 5 and Jetson Orin. You'll finish with a containerized, CI-tested robot and results you can measure and put on your resume.
Skills You Will Walk Away With
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1
Distributed Robotics Systems / Networking
- Configuring a multi-machine ROS 2 ARM Pi 5 + NVIDIA Jetson
- DDS discovery
- DDS Quality-of-Service tuning
- Diagnosing which QoS mismatch
- Static IP assignment on a robot LAN
- Multi-host process orchestration
- Headless operations of embedded devices
- NTP/chrony
- Camera streams
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2
Computer Vision
- OpenCV in Python and API via the port work
- Camera capture pipelines and V4L2 device handling
- Camera calibration
- Color-space conversion
- Image preprocessing for inference
- Object detection inference
- RViz2 image panels
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3
GPU Acceleration & Edge AI
- CUDA programming model — kernels, threads, blocks, grids, warps
- GPU memory hierarchy
- CuPy for GPU-accelerated array computation (drop-in NumPy replacement)
- Numba JIT compilation and custom kernels
- Host↔device memory transfer management
- TensorRT engine building and ONNX model
- Post-training quantization
- Jetson power modes
- NVIDIA Jetson Orin platform and JetPack SDK
- Thermal and power-envelope management
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4
Sensor Fusion
- Time-synchronizing heterogeneous sensor streams
- Handling variable-rate sensors
- TF2 transform trees applied to fusion
- Designing a perception
- Combining vision detections with range/odometry data into a single actionable state
- Reasoning about sensor confidence and disagreement
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5
SLAM & Mapping
- SLAM Toolbox
- Occupancy grid representation
- Costmap generation
- Saving, serializing, and reloading maps
- Localization against a known map (AMCL)
- Loop closure
- Odometry drift measurement
- Diagnosing a map
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6
Path Planning & Autonomous Navigation
- Dijkstra and A* algorithms
- Heuristic design
- Grid-based planning
- Nav2 stack architecture
- Behavior Trees
- Recovery behaviors
- Costmap2D layer configuration
- Local controller tuning (DWB / MPPI)
- Dynamic obstacle avoidance
- ROS 2 action
Schedule & Enrollment
| Date | Time |
| Nov 30 | 10:00 a.m. - 05:00 p.m. |
| Dec 01 | 10:00 a.m. - 05:00 p.m. |
| Dec 02 | 10:00 a.m. - 05:00 p.m. |
| Dec 03 | 10:00 a.m. - 05:00 p.m. |
| Dec 04 | 10:00 a.m. - 05:00 p.m. |
Instructors
Art Yudin
Questions? Check here
Yes. This is a hands-on course where you build a real autonomous robot, so you'll need the following hardware: NVIDIA Jetson Orin, Raspberry Pi 5, Mobile robot base, 2D LiDAR, CSI camera for computer vision. We will get you the full list with recommended items before the course starts, estimated at about $650–800.
You'll need a working knowledge of Python and ROS 2. You should be comfortable writing Python scripts, working with classes and packages, and building basic ROS 2 nodes that publish, subscribe, and use launch files. Recommended: This course builds on two earlier courses, and taking them first will help a lot:
Embedded Systems and Robotics Software covers Linux on embedded boards, working with hardware, and the basics of robotics software. ROS 2 Robotics Developer covers nodes, topics, services, actions.
Yes. This is a hands-on course where you build a real autonomous robot, so you'll need the following hardware: NVIDIA Jetson Orin, Raspberry Pi 5, Mobile robot base, 2D LiDAR, CSI camera for computer vision. We will get you the full list with recommended items before the course starts, estimated at about $650–800.
You'll need a working knowledge of Python and ROS 2. You should be comfortable writing Python scripts, working with classes and packages, and building basic ROS 2 nodes that publish, subscribe, and use launch files. Recommended: This course builds on two earlier courses, and taking them first will help a lot: Embedded Systems and Robotics Software covers Linux on embedded boards, working with hardware, and the basics of robotics software. ROS 2 Robotics Developer covers nodes, topics, services, actions.