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Course Outline
Introduction to Edge AI in Robotics
- Defining Edge AI
- The importance of Edge AI for robotics
- Challenges associated with real-time AI in autonomous systems
Deploying AI Models on Edge Devices
- Performing AI inference on NVIDIA Jetson and other edge hardware
- Utilizing TensorFlow Lite and ONNX for edge deployment
- Optimizing AI models for real-time execution
Real-Time Perception for Autonomous Systems
- Applying computer vision for robotic navigation
- Sensor fusion: Utilizing LiDAR, cameras, and IMUs
- Employing Edge AI for object detection and tracking
Decision-Making and Control in Robotics
- Implementing reinforcement learning for autonomous behaviors
- Path planning and obstacle avoidance strategies
- Optimizing latency in real-time AI systems
Integrating AI with ROS (Robot Operating System)
- An overview of ROS and its ecosystem
- Running AI-based perception models within ROS
- Applying Edge AI in multi-robot and swarm robotics scenarios
Optimizing AI for Low-Power Robotic Systems
- Efficient neural network architectures tailored 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
- Emerging advancements in Edge AI for robotics
Summary and Next Steps
Requirements
- Familiarity with AI and machine learning models
- Experience working with embedded systems or robotics
- Foundational knowledge of real-time computing
Target Audience
- Robotics engineers
- AI developers
- Automation specialists
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
