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Duration 21 hours
Course Outline
Foundations of TinyML in Robotics
- Core capabilities and limitations of TinyML
- The significance of edge AI in autonomous systems
- Hardware requirements for mobile robots and drones
Embedded Hardware and Sensor Integration
- Microcontrollers and embedded boards suitable for robotics
- Connecting cameras, IMUs, and proximity sensors
- Managing energy and computational budgets
Data Engineering for Robotic Perception
- Acquiring and labeling data for specific robotics tasks
- Techniques for signal and image preprocessing
- Strategies for feature extraction on constrained devices
Model Development and Optimization
- Choosing architectures for perception, detection, and classification
- Establishing training pipelines for embedded ML
- Optimizing through model compression, quantization, and latency reduction
On-Device Perception and Control
- Executing inference on microcontrollers
- Combining TinyML outputs with control algorithms
- Ensuring real-time safety and responsiveness
Enhancing Autonomous Navigation
- Implementing lightweight vision-based navigation
- Detecting and avoiding obstacles
- Maintaining environmental awareness within resource limits
Testing and Validating TinyML-Driven Robots
- Utilizing simulation tools and field testing methods
- Evaluating performance metrics for embedded autonomy
- Debugging and iterative refinement
Integration into Robotics Platforms
- Implementing TinyML within ROS-based workflows
- Connecting ML models to motor controllers
- Ensuring reliability across different hardware variants
Summary and Future Directions
Requirements
- A solid grasp of robotics system architectures
- Hands-on experience with embedded development
- Knowledge of core machine learning concepts
Target Audience
- Robotics engineers
- AI researchers
- Embedded developers
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.