AI-Powered Robotics: Autonomous Systems & Automation Training Course

Introduction

The integration of Artificial Intelligence (AI) with robotics is ushering in a new era of autonomous systems and advanced automation, profoundly impacting industries from manufacturing and healthcare to logistics and exploration. This AI-Powered Robotics: Autonomous Systems & Automation Training Course is meticulously designed for robotics engineers, software developers, AI/ML specialists, and automation professionals eager to build, program, and deploy intelligent robots capable of perception, decision-making, and complex task execution. The program bridges the gap between theoretical AI concepts and their practical application in real-world robotic systems.

Participants will gain hands-on experience with fundamental aspects of robot programming, including the Robot Operating System (ROS), and delve into crucial AI topics such as robot perception (computer vision), machine learning for control, autonomous navigation, and human-robot interaction (HRI). The curriculum emphasizes designing robots that can learn, adapt, and operate intelligently in dynamic environments. By mastering the synergy of AI and robotics, you will be equipped to develop next-generation intelligent robots and drive innovation in industrial automation, mobile robotics, and various other sectors, contributing to the advancement of truly autonomous systems.

Target Audience

• Robotics Engineers and Researchers.
• Software Developers working on robotic applications.
• AI/ML Engineers specializing in embodied AI.
• Automation and Control System Engineers.
• Manufacturing and Industrial Automation Professionals.
• Embedded Systems Developers interested in intelligent hardware.

Duration

10 days

Course Objectives

1. Understand the foundational principles of robotics and the role of AI in creating autonomous systems.
2. Gain proficiency in using the Robot Operating System (ROS) for robot development and integration.
3. Apply AI-driven computer vision techniques for robot perception and environmental understanding.
4. Implement algorithms for robot localization, mapping (SLAM), and autonomous navigation.
5. Explore the application of machine learning, including reinforcement learning, for robot control and task learning.
6. Understand the principles and challenges of Human-Robot Interaction (HRI) for collaborative environments.
7. Discuss the ethical, safety, and societal implications of deploying AI-powered robots.
8. Develop practical skills in designing and building intelligent robotic solutions for real-world problems.

Course Content

Module 1. Foundations of AI-Powered Robotics

• Introduction to Robotics: Definition, components (sensors, actuators, controllers), types of robots
• The evolution of robotics and the impact of Artificial Intelligence
• Levels of robot autonomy: From teleoperation to full autonomy
• Core concepts of intelligent systems in robotics
• Overview of common robotic platforms and their applications

Module 2. Robot Operating System (ROS) Fundamentals

• Introduction to ROS: Architecture, nodes, topics, services, messages, actions
• Setting up a ROS environment and basic ROS commands
• Programming robots with ROS in Python and C++
• Using ROS tools: RViz for visualization, Gazebo for simulation
• Building and managing ROS packages for modular development

Module 3. Robot Kinematics and Dynamics

• Introduction to Kinematics: Describing robot motion
• Forward Kinematics: Calculating end-effector position from joint angles
• Inverse Kinematics: Determining joint angles for a desired end-effector position
• Introduction to Robot Dynamics: Forces and torques in robot motion
• Motion planning basics: Trajectory generation and collision avoidance

Module 4. Robot Perception with AI

• Principles of robot sensing: Vision, LiDAR, ultrasonic, force sensors
• Computer Vision for Robotics: Object detection, recognition, and tracking
• Scene understanding and semantic segmentation using deep learning
• Sensor fusion techniques: Combining data from multiple sensors for robust perception
• Introduction to point cloud processing for 3D environment mapping

Module 5. Robot Navigation and Localization

• Simultaneous Localization and Mapping (SLAM): Building maps while localizing the robot
• Localization techniques: Odometry, GPS, visual localization
• Path Planning algorithms: A*, Dijkstra, RRT for obstacle avoidance
• Autonomous navigation stacks: ROS Navigation Stack
• Environment representation for navigation: Occupancy grids, costmaps

Module 6. Machine Learning for Robot Control

• Applying Supervised Learning for robot task recognition and classification
• Unsupervised Learning for discovering patterns in robot sensor data
• Reinforcement Learning (RL) for learning complex robot behaviors
• Imitation Learning: Teaching robots by demonstration
• Integrating ML models for adaptive and intelligent robot control

Module 7. Human-Robot Interaction (HRI)

• Principles of Human-Robot Interaction: Designing intuitive and safe interfaces
• Collaborative Robots (Cobots): Working alongside humans
• Natural Language Processing (NLP) for robot voice commands and dialogue systems
• Gesture recognition and emotional intelligence in robots
• Building trust and ensuring natural communication in HRI

Module 8. Robotic Process Automation (RPA) and AI

• Introduction to Robotic Process Automation (RPA): Automating business processes
• The synergy of RPA with AI for intelligent automation
• Identifying suitable processes for AI-enhanced RPA
• Integrating ML models into RPA workflows for decision-making
• Case studies of AI-powered RPA in enterprise settings

Module 9. Industrial Robotics and Automation

• Applications of robots in manufacturing: Assembly, welding, painting, material handling
• Industrial robot types: Robotic arms (e.g., manipulators), AGVs, mobile robots
• Programming industrial robots: Teach pendants, offline programming
• Safety standards and regulations in industrial robotics
• The concept of Industry 4.0 and smart factories with AI-driven robotics

Module 10. Mobile Robotics and Autonomous Vehicles

• Overview of mobile robot types: Wheeled, legged, aerial (drones)
• Autonomous navigation for mobile robots in complex environments
• Introduction to self-driving car technology: Perception, planning, control
• Applications of autonomous drones in mapping, delivery, and inspection
• Challenges and future of outdoor and large-scale autonomous systems

Module 11. Ethical and Safety Considerations in Robotics

• Ethical dilemmas in robotics: Accountability, job displacement, human dignity
• Robot safety standards and risk assessment (ISO 10218, ISO/TS 15066)
• The 'Robotics Laws' and their modern interpretation
• Addressing bias in robot perception and decision-making
• Responsible design and deployment of robotic systems

Module 12. Building an AI-Powered Robot Project

• Project lifecycle for a robotic system: Design, development, testing, deployment
• System integration: Bringing together hardware and software components
• Debugging and troubleshooting robotic systems
• Practical exercises in building a simple AI-powered robot simulation or prototype
• Presenting and evaluating a final AI-robotics project

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank