Course Outline

Introduction to Edge AI in Robotics

  • What is Edge AI?
  • Why Edge AI is essential for robotics
  • Challenges of real-time AI in autonomous systems

Deploying AI Models on Edge Devices

  • AI inference on NVIDIA Jetson and other edge hardware
  • Using TensorFlow Lite and ONNX for edge deployment
  • Optimizing AI models for real-time execution

Real-Time Perception for Autonomous Systems

  • Computer vision for robotic navigation
  • Sensor fusion: LiDAR, cameras, and IMUs
  • Edge AI for object detection and tracking

Decision-Making and Control in Robotics

  • Reinforcement learning for autonomous behaviors
  • Path planning and obstacle avoidance
  • Latency optimization in real-time AI systems

Integrating AI with ROS (Robot Operating System)

  • Overview of ROS and its ecosystem
  • Running AI-based perception models in ROS
  • Edge AI in multi-robot and swarm robotics applications

Optimizing AI for Low-Power Robotic Systems

  • Efficient neural network architectures for robotics
  • Reducing power consumption in AI-driven robots
  • Deploying AI on battery-powered robotic platforms

Real-World Applications and Future Trends

  • Autonomous drones and industrial robots
  • AI-powered robotic assistants
  • Future advancements in Edge AI for robotics

Summary and Next Steps

Requirements

  • An understanding of AI and machine learning models
  • Experience with embedded systems or robotics
  • Basic knowledge of real-time computing

Audience

  • Robotics engineers
  • AI developers
  • Automation specialists
 21 Hours

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