Smart Robotics in Manufacturing: AI for Perception, Planning, and Control Training Course

AI-powered predictive maintenance leverages machine learning and data analytics to forecast equipment failures and optimise maintenance schedules. It transforms reactive maintenance models into proactive strategies, enabling improved uptime, cost reduction, and extended asset longevity.

This instructor-led, live training (available online or on-site) is designed for intermediate-level professionals seeking to implement AI-driven predictive maintenance solutions within industrial environments.

By the end of this training, participants will be able to:

• Understand how predictive maintenance differs from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-powered analysis.
• Apply machine learning models to detect anomalies and predict failures.
• Implement end-to-end workflows from sensor data to actionable insights.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises and real-world case studies.
• Live demonstrations and practical data workflows.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

AI for Process Optimisation involves applying machine learning and data analytics to enhance efficiency, quality, and throughput in manufacturing operations.

This instructor-led, live training (available online or on-site) is designed for intermediate-level manufacturing professionals who wish to apply AI techniques to streamline operations, reduce downtime, and support continuous improvement initiatives.

By the end of this training, participants will be able to:

• Understand AI concepts relevant to manufacturing optimisation.
• Collect and prepare production data for analysis.
• Apply machine learning models to identify bottlenecks and predict failures.
• Visualise and interpret results to support data-driven decisions.

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

AI for Quality Control involves the use of computer vision and machine learning techniques to identify defects, anomalies, and deviations within production processes.

This instructor-led, live training (available online or on-site) is designed for beginner to intermediate-level quality professionals who wish to apply AI tools to automate inspections and enhance product quality in manufacturing settings.

By the end of this training, participants will be able to:

• Understand how AI is applied in industrial quality control.
• Collect and label image or sensor data from production lines.
• Use machine learning and computer vision to detect defects.
• Develop simple AI models for anomaly detection and yield forecasting.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

AI in Supply Chain and Manufacturing Logistics refers to the application of predictive analytics, machine learning, and automation to optimise inventory management, route planning, and demand forecasting.

This instructor-led, live training (available online or on-site) is designed for intermediate-level supply chain professionals who wish to leverage AI-driven tools to enhance logistics performance, forecast demand with greater accuracy, and automate warehouse and transport operations.

By the end of this training, participants will be able to:

• Understand how AI is applied across logistics and supply chain activities.
• Use machine learning models for demand forecasting and inventory control.
• Analyse routes and optimise transport using AI-based techniques.
• Automate decision-making in warehouses and fulfilment processes.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

AI in Smart Factories refers to the application of artificial intelligence to automate, monitor, and optimise industrial operations in real time.

This instructor-led, live training (available online or on-site) is designed for beginner-level decision-makers and technical leads seeking a strategic and practical introduction to leveraging AI within smart factory environments.

By the end of this training, participants will be able to:

• Understand the core principles of AI and machine learning.
• Identify key AI use cases in manufacturing and automation.
• Explore how AI supports predictive maintenance, quality control, and process optimisation.
• Evaluate the steps involved in launching AI-driven initiatives.

Course Format

• Interactive lectures and discussions.
• Real-world case studies and group exercises.
• Strategic frameworks and implementation guidance.

Course Customisation Options

• To request a customised training session for this course, please contact us to make arrangements.

AI Use Case Implementation is a hands-on, project-driven approach to applying machine learning, computer vision, and data analytics to solve real-world industrial challenges using actual or simulated datasets.

This instructor-led, live training (online or onsite) is aimed at intermediate-level cross-functional teams who wish to collaboratively implement AI use cases aligned with their operational goals and gain experience working with industrial data pipelines.

By the end of this training, participants will be able to:

• Select and scope practical AI use cases from operations, quality, or maintenance.
• Work collaboratively across roles to develop machine learning solutions.
• Handle, clean, and analyse diverse industrial datasets.
• Present a working prototype of an AI-enabled solution based on a selected use case.

Format of the Course

• Interactive lecture and discussion.
• Group-based exercises and project work.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

Azure Bot Service combines the capabilities of the Microsoft Bot Framework and Azure Functions, delivering a robust platform for rapidly building intelligent bots.

In this instructor-led, live training, participants will explore how to efficiently develop intelligent bots using Microsoft Azure.

By the end of the training, participants will be able to:

Grasp the core concepts underpinning intelligent bots.

Build intelligent bots using cloud-based applications.

Gain practical expertise in the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Apply proven bot design patterns to real-world scenarios.

Create and deploy their first intelligent bot using Microsoft Azure.

Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals with an interest in bot development.

Format of the course

The training blends lectures and discussions with hands-on exercises, placing a strong emphasis on practical application.

A bot, or chatbot, is like a digital assistant designed to automate user interactions across various messaging platforms, enabling tasks to be completed faster without requiring human intervention.

In this instructor-led, live training, participants will learn how to begin developing their own bots by creating sample chatbots using industry-standard bot development tools and frameworks.

By the end of this training, participants will be able to:

• Understand the diverse uses and applications of bots
• Grasp the end-to-end process of bot development
• Explore the range of tools and platforms used in building bots
• Build a sample chatbot for Facebook Messenger
• Build a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bots

Course Format

• A blend of lecture, discussion, hands-on exercises, and practical application

Digital Twins are virtual replicas of physical systems, enhanced by real-time data and AI-driven intelligence.

This instructor-led, live training (available online or on-site) is designed for intermediate-level professionals who wish to build, deploy, and optimise digital twin models using real-time data and AI-based insights.

By the end of this training, participants will be able to:

• Understand the architecture and components of digital twins.
• Use simulation tools to model complex systems and environments.
• Integrate real-time data streams into virtual models.
• Apply AI techniques for predictive behaviour and anomaly detection.

Course Format

• Interactive lectures and discussions.
• Plenty of exercises and practical sessions.
• Hands-on implementation in a live lab environment.

Course Customisation Options

• To request a customised version of this course, please contact us to arrange.

Edge AI involves deploying artificial intelligence models directly on devices and machines at the edge of the network, enabling real-time decision-making with minimal latency.

This instructor-led, live training (available online or on-site) is designed for advanced-level embedded and IoT professionals seeking to deploy AI-powered logic and control systems within manufacturing environments where speed, reliability, and offline operation are critical.

By the end of this training, participants will be able to:

• Understand the architecture and benefits of edge AI systems.
• Build and optimise AI models for deployment on embedded devices.
• Use tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Practical implementation in a live-lab environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

Industrial computer vision with AI is transforming how manufacturers and QA teams detect surface defects, verify part conformity, and automate visual inspection processes.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level QA teams, automation engineers, and developers who wish to design and implement computer vision systems for defect detection and inspection using AI techniques.

By the end of this training, participants will be able to:

• Understand the architecture and components of industrial vision systems.
• Build AI models for visual defect detection using deep learning.
• Integrate real-time inspection pipelines with industrial cameras and devices.
• Deploy and optimise AI-powered inspection systems for production environments.

Format of the Course

• Interactive lecture and discussion.
• Plenty of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

A Smart Robot is an Artificial Intelligence (AI) system capable of learning from its environment and experiences, continuously enhancing its capabilities through that knowledge. Smart Robots are designed to collaborate with humans, working alongside them and learning from their behaviour. Moreover, they are equipped to perform not only manual tasks but also cognitive functions. Beyond physical robots, Smart Robots can exist purely as software applications, residing within a computer without moving parts or direct physical interaction with the external world.

In this instructor-led, live training programme, participants will explore the diverse technologies, frameworks, and techniques used to program various types of mechanical Smart Robots. They will then apply this knowledge to complete their own Smart Robot projects.

The course is structured into four sections, each spanning three days of lectures, discussions, and hands-on robot development in a live lab environment. Each section concludes with a practical, hands-on project, enabling participants to practice and demonstrate their newly acquired skills.

The target hardware for this course will be simulated in 3D using specialised simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be used to programme the robots.

By the end of this training, participants will be able to:

• Grasp the key concepts underpinning robotic technologies
• Understand and manage the interaction between software and hardware within a robotic system
• Comprehend and implement the software components that form the foundation of Smart Robots
• Build and operate a simulated mechanical Smart Robot capable of seeing, sensing, processing, grasping, navigating, and interacting with humans via voice
• Enhance a Smart Robot's ability to perform complex tasks through Deep Learning
• Test and troubleshoot a Smart Robot in realistic scenarios

Target Audience

• Developers
• Engineers

Course Format

• A blend of lecture, discussion, exercises, and intensive hands-on practice

Note

• To customise any aspect of this course (programming language, robot model, etc.), please contact us to arrange.