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Duration 21 hours
Course Outline
Foundations of TinyML in Robotics
- Key capabilities and inherent constraints of TinyML.
- The role of edge AI within autonomous systems.
- Hardware considerations for mobile robots and drones.
Embedded Hardware and Sensor Interfaces
- Microcontrollers and embedded boards suitable for robotics.
- Integrating cameras, IMUs, and proximity sensors.
- Managing energy and compute budgets effectively.
Data Engineering for Robotic Perception
- Collecting and labeling data for specific robotics tasks.
- Techniques for signal and image preprocessing.
- Feature extraction strategies tailored for constrained devices.
Model Development and Optimization
- Selecting appropriate architectures for perception, detection, and classification.
- Building training pipelines for embedded ML.
- Applying model compression, quantization, and latency optimization.
On-Device Perception and Control
- Executing inference directly on microcontrollers.
- Fusing TinyML outputs with control algorithms.
- Ensuring real-time safety and system responsiveness.
Enhancing Autonomous Navigation
- Implementing lightweight vision-based navigation.
- Developing obstacle detection and avoidance mechanisms.
- Maintaining environmental awareness under resource constraints.
Testing and Validation of TinyML-Driven Robots
- Utilizing simulation tools and field testing approaches.
- Evaluating performance metrics for embedded autonomy.
- Debugging and driving iterative improvement.
Integration into Robotics Platforms
- Deploying TinyML within ROS-based pipelines.
- Interfacing ML models with motor controllers.
- Maintaining reliability across diverse hardware variations.
Summary and Next Steps
Requirements
- A solid understanding of robotics system architectures.
- Practical experience with embedded development.
- Familiarity with core machine learning concepts.
Intended 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.
