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Course Outline
Fundamentals of Edge AI in Robotics
- Defining Edge AI
- The critical need for Edge AI in robotics
- Hurdles in real-time AI for autonomous platforms
Implementing AI Models on Edge Hardware
- Executing AI inference on NVIDIA Jetson and similar edge devices
- Leveraging TensorFlow Lite and ONNX for edge deployment
- Streamlining AI models for real-time processing
Real-Time Perception in Autonomous Contexts
- Computer vision techniques for robotic navigation
- Sensor integration: LiDAR, cameras, and IMUs
- Applying Edge AI to object detection and tracking
Robotics Control and Decision Logic
- Utilizing reinforcement learning for autonomous actions
- Strategy for path planning and obstacle evasion
- Minimizing latency in real-time AI frameworks
AI Integration with ROS (Robot Operating System)
- Introduction to ROS and its surrounding ecosystem
- Operating AI-based perception models within ROS
- Edge AI applications in multi-robot and swarm scenarios
Energy-Efficient AI for Robotic Systems
- High-performance neural network designs for robotics
- Lowering energy use in AI-driven robots
- Running AI on battery-operated robotic platforms
Practical Applications and Emerging Trends
- Autonomous drones and industrial robotic units
- AI-driven robotic assistants
- Future developments in robotics-focused Edge AI
Recap and Forward Path
Requirements
- Familiarity with AI and machine learning frameworks
- Practical experience with embedded systems or robotics
- Foundational understanding of real-time computing
Target Audience
- Robotics engineers
- AI developers
- Automation specialists
21 Hours
Testimonials (1)
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