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Course Outline

Fundamentals of Edge AI in Robotics

  • Defining Edge AI
  • The critical role of Edge AI in robotics
  • Obstacles facing real-time AI in autonomous setups

Implementing AI Models on Edge Hardware

  • Executing AI inference on NVIDIA Jetson and comparable edge devices
  • Leveraging TensorFlow Lite and ONNX for edge deployment
  • Tuning AI models for immediate execution

Instant Perception for Autonomous Platforms

  • Applying computer vision to robotic navigation
  • Combining sensors: LiDAR, cameras, and IMUs
  • Utilizing Edge AI for detecting and tracking objects

Cognitive Control and Decision Logic in Robotics

  • Employing reinforcement learning for autonomous actions
  • Strategic path planning and obstacle evasion
  • Minimizing latency in real-time AI architectures

Merging AI with the Robot Operating System (ROS)

  • An overview of ROS and its surrounding ecosystem
  • Operating AI-based perception models within ROS
  • Edge AI applications in multi-robot and swarm contexts

Optimizing AI for Energy-Efficient Robotic Systems

  • Streamlined neural network designs for robotics
  • Lowering energy usage in AI-integrated robots
  • Deploying AI on battery-operated robotic units

Practical Uses and Emerging Trends

  • Self-operating drones and industrial automation
  • Intelligent robotic assistants powered by AI
  • Upcoming developments in robotics Edge AI

Recap and Future Directions

Requirements

  • Familiarity with AI and machine learning frameworks
  • Practical experience in embedded systems or robotics
  • Foundational grasp of real-time computing principles

Target Audience

  • Robotics Engineers
  • AI Developers
  • Automation Specialists
 21 Hours

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