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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
Testimonials (1)
That we can cover advance topic and work with real-life example