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Duration 21 hours
Course Outline
Foundations of TinyML for Robotics
- Core 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 suited for robotics.
- Integration of cameras, IMUs, and proximity sensors.
- Balancing energy and compute budgets.
Data Engineering for Robotic Perception
- Collecting and labeling data for specific robotics tasks.
- Signal and image preprocessing techniques.
- Feature extraction strategies for constrained devices.
Model Development and Optimization
- Selecting architectures for perception, detection, and classification.
- Training pipelines tailored for embedded ML.
- Model compression, quantization, and latency optimization.
On-Device Perception and Control
- Executing inference on microcontrollers.
- Fusing TinyML outputs with control algorithms.
- Ensuring real-time safety and responsiveness.
Autonomous Navigation Enhancements
- Lightweight vision-based navigation.
- Obstacle detection and avoidance strategies.
- Maintaining environmental awareness under resource constraints.
Testing and Validation of TinyML-Driven Robots
- Simulation tools and field testing approaches.
- Performance metrics for embedded autonomy.
- Debugging processes and iterative improvement.
Integration into Robotics Platforms
- Deploying TinyML within ROS-based pipelines.
- Interfacing ML models with motor controllers.
- Maintaining reliability across hardware variations.
Summary and Next Steps
Requirements
- A solid grasp of robotics system architectures.
- Practical experience in embedded development.
- Familiarity with 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.