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Course Outline
Introduction to Edge AI in Robotics
- Defining Edge AI.
- The critical importance of Edge AI for robotics.
- Addressing challenges associated with real-time AI in autonomous systems.
Deploying AI Models on Edge Devices
- Executing AI inference on NVIDIA Jetson and other edge hardware platforms.
- 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.
- Implementing sensor fusion techniques using LiDAR, cameras, and IMUs.
- Leveraging Edge AI for object detection and tracking.
Decision-Making and Control in Robotics
- Employing reinforcement learning for autonomous behaviors.
- Managing path planning and obstacle avoidance strategies.
- Optimizing latency within real-time AI systems.
Integrating AI with ROS (Robot Operating System)
- Providing an overview of ROS and its broader ecosystem.
- Running AI-based perception models within the ROS framework.
- Exploring the role of Edge AI in multi-robot and swarm robotics applications.
Optimizing AI for Low-Power Robotic Systems
- Designing efficient neural network architectures suited for robotics.
- Strategies for reducing power consumption in AI-driven robots.
- Deploying AI capabilities on battery-powered robotic platforms.
Real-World Applications and Future Trends
- Examining autonomous drones and industrial robots.
- The rise of AI-powered robotic assistants.
- Looking ahead at future advancements in Edge AI for robotics.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning models.
- Prior experience working with embedded systems or robotics.
- Basic knowledge of real-time computing principles.
Target Audience
- Robotics engineers.
- AI developers.
- Automation specialists.
21 Hours
Testimonials (1)
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