Edge AI and Robotics: Enabling Autonomous Systems

• 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

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.

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