Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Edge AI in Robotics
- Defining Edge AI.
- The critical importance of Edge AI for robotics.
- Challenges associated with real-time AI in autonomous systems.
Deploying AI Models on Edge Devices
- Performing AI inference on NVIDIA Jetson and other edge hardware.
- Utilizing TensorFlow Lite and ONNX for edge deployment.
- Optimizing AI models for real-time execution.
Real-Time Perception for Autonomous Systems
- Applying computer vision to robotic navigation.
- Implementing sensor fusion with LiDAR, cameras, and IMUs.
- Leveraging Edge AI for object detection and tracking.
Decision-Making and Control in Robotics
- Employing reinforcement learning for autonomous behaviors.
- Executing path planning and obstacle avoidance strategies.
- Optimizing latency in real-time AI systems.
Integrating AI with ROS (Robot Operating System)
- Overview of ROS and its ecosystem.
- Running AI-based perception models within ROS.
- Applying Edge AI in multi-robot and swarm robotics contexts.
Optimizing AI for Low-Power Robotic Systems
- Implementing efficient neural network architectures for robotics.
- Strategies for 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
- Familiarity with AI and machine learning models.
- Prior experience with embedded systems or robotics.
- Fundamental knowledge of real-time computing.
Target Audience
- Robotics engineers.
- AI developers.
- Automation specialists.
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
