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Duration 21 hours
Course Outline
Introduction to Edge AI in Robotics
- Defining Edge AI
- The necessity of Edge AI in robotic systems
- Challenges associated with real-time AI in autonomous contexts
Deploying AI Models on Edge Devices
- Performing AI inference on NVIDIA Jetson and other edge hardware
- Utilizing TensorFlow Lite and ONNX for edge deployment strategies
- Tuning AI models for optimal real-time execution
Real-Time Perception for Autonomous Systems
- Applying computer vision to robotic navigation
- Sensor fusion techniques using LiDAR, cameras, and IMUs
- Leveraging Edge AI for object detection and tracking
Decision-Making and Control in Robotics
- Reinforcement learning for fostering autonomous behaviors
- Strategies for path planning and obstacle avoidance
- Reducing latency in real-time AI systems
Integrating AI with ROS (Robot Operating System)
- Overview of ROS and its broader ecosystem
- Executing AI-based perception models within the ROS framework
- The role of Edge AI in multi-robot and swarm robotics scenarios
Optimizing AI for Low-Power Robotic Systems
- Designing efficient neural network architectures for robotics
- Minimizing power consumption in AI-driven robotic platforms
- Deploying AI on battery-operated robotic devices
Real-World Applications and Future Trends
- Use cases in autonomous drones and industrial robotics
- Development of AI-powered robotic assistants
- Emerging advancements in Edge AI for robotics
Summary and Next Steps
Requirements
- Solid comprehension of AI and machine learning models
- Practical experience with embedded systems or robotics
- Fundamental knowledge of real-time computing concepts
Target Audience
- Robotics engineers
- AI developers
- Automation specialists
Testimonials (1)
That we can cover advance topic and work with real-life example
