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Duration 21 hours
Course Outline
TinyML Foundations for Robotics
- Core capabilities and limitations of TinyML
- The function of edge AI within 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 consumption and compute resources
Data Engineering for Robotic Perception
- Collection and labeling of data for robotic tasks
- Signal and image preprocessing methods
- Feature extraction strategies for resource-constrained devices
Model Development and Optimization
- Choosing architectures for perception, detection, and classification
- Training pipelines tailored for embedded ML
- Model compression, quantization, and latency refinement
On-Device Perception and Control
- Executing inference on microcontrollers
- Combining TinyML outputs with control algorithms
- Ensuring real-time safety and system responsiveness
Enhancing Autonomous Navigation
- Lightweight, vision-based navigation techniques
- Obstacle detection and avoidance mechanisms
- Maintaining environmental awareness under resource constraints
Testing and Validation of TinyML-Driven Robots
- Simulation tools and field testing methodologies
- Performance metrics for embedded autonomy
- Debugging strategies and iterative improvement
Integration into Robotics Platforms
- Implementing TinyML within ROS-based pipelines
- Connecting ML models with motor controllers
- Ensuring reliability across different hardware variations
Summary and Next Steps
Requirements
- A solid grasp of robotics system architectures
- Practical experience with embedded development
- Working knowledge of 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.