Edge AI and Robotics: Enabling Autonomous Systems Training Course
Edge AI is revolutionizing robotics by enabling real-time decision-making in autonomous systems.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level robotics engineers, AI developers, and automation specialists who wish to implement Edge AI for robotics applications.
By the end of this training, participants will be able to:
- Understand the role of Edge AI in autonomous systems.
- Deploy AI models on edge devices for real-time robotics.
- Optimize AI performance for low-latency decision-making.
- Integrate computer vision and sensor fusion for robotic autonomy.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
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