Course Outline
Foundations of TinyML in Robotics
- Essential capabilities and constraints of TinyML
- The role of edge AI in autonomous systems
- Hardware considerations for mobile robots and drones
Embedded Hardware and Sensor Interfaces
- Microcontrollers and embedded boards suitable for robotics
- Integration of cameras, IMUs, and proximity sensors
- Managing energy and compute budgets
Data Engineering for Robotic Perception
- Collecting and labeling data for specific robotics tasks
- Signal and image preprocessing methods
- Feature extraction strategies for constrained devices
Model Development and Optimization
- Choosing architectures for perception, detection, and classification
- Training pipelines for embedded ML
- Model compression, quantization, and latency optimization
On-Device Perception and Control
- Executing inference on microcontrollers
- Combining TinyML outputs with control algorithms
- Ensuring real-time safety and responsiveness
Autonomous Navigation Enhancements
- Implementing lightweight vision-based navigation
- Obstacle detection and avoidance techniques
- Maintaining environmental awareness under resource constraints
Testing and Validation of TinyML-Driven Robots
- Utilizing simulation tools and field testing approaches
- Defining performance metrics for embedded autonomy
- Debugging and iterative improvement processes
Integration into Robotics Platforms
- Deploying TinyML within ROS-based pipelines
- Interfacing ML models with motor controllers
- Ensuring reliability across varying hardware conditions
Summary and Next Steps
Requirements
- A solid understanding of robotics system architectures
- Practical experience with embedded development
- Familiarity with core machine learning concepts
Target Audience
- Robotics engineers
- AI researchers
- Embedded developers
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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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.
