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Course Outline
Introduction to Edge AI in Robotics
- What is Edge AI?
- Why Edge AI is essential for robotics
- Challenges of real-time AI in autonomous systems
Deploying AI Models on Edge Devices
- AI inference on NVIDIA Jetson and other edge hardware
- Using TensorFlow Lite and ONNX for edge deployment
- Optimizing AI models for real-time execution
Real-Time Perception for Autonomous Systems
- Computer vision for robotic navigation
- Sensor fusion: LiDAR, cameras, and IMUs
- Edge AI for object detection and tracking
Decision-Making and Control in Robotics
- Reinforcement learning for autonomous behaviors
- Path planning and obstacle avoidance
- Latency optimization in real-time AI systems
Integrating AI with ROS (Robot Operating System)
- Overview of ROS and its ecosystem
- Running AI-based perception models in ROS
- Edge AI in multi-robot and swarm robotics applications
Optimizing AI for Low-Power Robotic Systems
- Efficient neural network architectures for robotics
- Reducing power consumption in AI-driven robots
- Deploying AI on battery-powered robotic platforms
Real-World Applications and Future Trends
- Autonomous drones and industrial robots
- AI-powered robotic assistants
- Future advancements in Edge AI for robotics
Summary and Next Steps
Requirements
- An understanding of AI and machine learning models
- Experience with embedded systems or robotics
- Basic knowledge of real-time computing
Audience
- Robotics engineers
- AI developers
- Automation specialists
21 Hours
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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