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Course Outline
Introduction to Edge AI in Robotics
- Defining Edge AI.
- The necessity of Edge AI in robotics.
- Challenges associated with real-time AI in autonomous systems.
Deploying AI Models on Edge Devices
- AI inference using 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
- Computer vision applications for robotic navigation.
- Sensor fusion: Integrating LiDAR, cameras, and IMUs.
- Leveraging Edge AI for object detection and tracking.
Decision-Making and Control in Robotics
- Applying 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)
- Overview of ROS and its ecosystem.
- Running AI-based perception models within ROS.
- Implementing Edge AI in multi-robot and swarm robotics applications.
Optimizing AI for Low-Power Robotic Systems
- Efficient neural network architectures tailored for robotics.
- Strategies for 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
- A solid understanding of AI and machine learning models.
- Practical experience with embedded systems or robotics.
- Fundamental knowledge of real-time computing.
Target Audience
- Robotics engineers.
- AI developers.
- Automation specialists.
21 Hours
Testimonials (1)
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