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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 in enhancing autonomous systems
- Hardware considerations specific to mobile robots and drones
Embedded Hardware and Sensor Interfaces
- Microcontrollers and embedded boards suitable for robotics
- Integration methods for cameras, IMUs, and proximity sensors
- Strategies for energy and compute budgeting
Data Engineering for Robotic Perception
- Methods for collecting and labeling data specific to robotics tasks
- Techniques for signal and image preprocessing
- Feature extraction strategies designed for constrained devices
Model Development and Optimization
- Selecting appropriate architectures for perception, detection, and classification
- Establishing training pipelines for embedded ML
- Applying model compression, quantization, and latency optimization
On-Device Perception and Control
- Executing inference processes on microcontrollers
- Fusing TinyML outputs with control algorithms
- Ensuring real-time safety and system responsiveness
Autonomous Navigation Enhancements
- 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
- Defining performance metrics for embedded autonomy
- Performing debugging and iterative system improvement
Integration into Robotics Platforms
- Deploying TinyML within ROS-based pipelines
- Interfacing ML models with motor controllers
- Maintaining system reliability across various hardware configurations
Summary and Next Steps
Requirements
- A solid understanding of robotics system architectures
- Prior 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.