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

AI-driven predictive maintenance utilises machine learning and data analytics to anticipate equipment failures and optimise maintenance schedules. This approach shifts organisations from reactive maintenance models to proactive strategies, facilitating improved operational uptime, reduced costs, and extended asset longevity.

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

Upon completion of this training, participants will be able to:

• Differentiate predictive maintenance from reactive and preventive maintenance strategies.
• Collect and structure machine data for AI-driven analysis.
• Apply machine learning models to identify anomalies and forecast failures.
• Implement end-to-end workflows that transform sensor data into actionable insights.

Course Format

• Interactive lectures and discussions.
• Practical exercises and real-world case studies.
• Live demonstrations and hands-on data workflow practice.

Course Customisation Options

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

AI for Process Optimisation involves applying machine learning and data analytics to boost efficiency, product quality, and throughput within manufacturing environments.

This instructor-led live training (available online or onsite) is designed for intermediate-level manufacturing professionals looking to utilise AI techniques to streamline operations, minimise downtime, and support continuous improvement efforts.

Upon completion of this training, participants will be able to:

• Grasp AI concepts pertinent 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 decision-making.

Course Format

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

Course Customisation Options

• To arrange bespoke training for this course, please contact us.

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

This instructor-led, live training is available both online and onsite. It is designed for quality professionals at beginner to intermediate levels who wish to apply AI tools to automate inspections and enhance product quality in manufacturing settings.

Upon completion of this training, participants will be able to:

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

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

AI in Supply Chain and Manufacturing Logistics involves applying predictive analytics, machine learning, and automation to optimise inventory levels, routing efficiency, and demand forecasting.

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

Upon completion of this training, participants will be able to:

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

Format of the Course

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

Course Customization Options

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

The integration of artificial intelligence into smart factories involves applying AI technologies to automate, monitor, and optimise industrial operations in real time.

This instructor-led live training, available either online or onsite, is designed for beginner-level decision-makers and technical leads who wish to gain a strategic and practical overview of how AI can be leveraged within smart factory environments.

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

• Comprehend the core principles of AI and machine learning.
• Identify key AI use cases within manufacturing and automation sectors.
• 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 for this course, please contact us to arrange.

AI Use Case Implementation employs a hands-on, project-based methodology to leverage machine learning, computer vision, and data analytics for addressing real-world industrial challenges, utilising actual or simulated datasets.

This instructor-led, live training (available online or onsite) targets intermediate-level cross-functional teams seeking to collaboratively implement AI solutions aligned with their operational objectives while gaining practical experience with industrial data pipelines.

Upon completion of this training, participants will be able to:

• Identify and scope practical AI use cases within operations, quality assurance, or maintenance.
• Collaborate across different roles to develop machine learning solutions.
• Manage, clean, and analyse diverse industrial datasets.
• Demonstrate a functional prototype of an AI-enabled solution based on a chosen use case.

Course Format

• Interactive lectures and discussions.
• Group-based exercises and project work.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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

Azure Bot Service unites the power of the Microsoft Bot Framework with Azure Functions, offering a robust platform for rapidly creating intelligent bots.

In this instructor-led, live training, participants will discover efficient methods for developing intelligent bots using Microsoft Azure.

Upon completing the training, participants will be capable of:

Grasping the fundamental concepts behind intelligent bots.

Constructing intelligent bots using cloud-based applications.

Acquiring practical knowledge of the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Applying established bot design patterns to real-world scenarios.

Creating and deploying their first intelligent bot using Microsoft Azure.

Target Audience

This course is tailored for developers, hobbyists, engineers, and IT professionals keen on bot development.

Course Format

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

A bot, or chatbot, acts as a digital assistant designed to automate user interactions across various messaging platforms, enabling quicker task completion without requiring direct human intervention.

Through this instructor-led live training, participants will learn the fundamentals of bot development by creating sample chatbots using dedicated development tools and frameworks.

Upon completion of this training, participants will be able to:

• Understand the various use cases and applications of bots
• Comprehend the end-to-end process of developing bots
• Explore the range of tools and platforms available for bot development
• Build a sample chatbot for Facebook Messenger
• Build a sample chatbot using the Microsoft Bot Framework

Audience

• Developers interested in creating their own bot

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Digital Twins serve as virtual replicas of physical systems, amplified by real-time data feeds and AI-driven intelligence.

This instructor-led, live training session (available online or onsite) is designed for intermediate-level professionals looking to build, deploy, and optimise digital twin models leveraging real-time data and AI-based insights.

Upon completion of this training, participants will be able to:

• Grasp the architecture and components of digital twins.
• Utilise 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.
• Numerous exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Customisation Options

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

Edge AI involves deploying artificial intelligence models directly onto devices and machines at the network's edge, facilitating real-time decision-making with minimal latency.

This instructor-led, live training (available online or onsite) is designed for advanced-level embedded and IoT professionals looking to implement AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline functionality are paramount.

Upon completion of this training, participants will be able to:

• Comprehend the architecture and advantages of edge AI systems.
• Construct and optimise AI models for deployment on embedded devices.
• Utilise 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 practical practice.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request tailored training for this course, please contact us to make arrangements.

The application of artificial intelligence in industrial computer vision is revolutionising the way manufacturers and quality assurance (QA) teams identify surface defects, verify part compliance, and automate visual inspection processes.

This instructor-led live training, available either online or onsite, is designed for intermediate to advanced QA teams, automation engineers, and developers who aim to design and implement computer vision systems for defect detection and inspection using AI techniques.

Upon completion of this training, participants will be capable of:

• Understanding the architecture and components of industrial vision systems.
• Building AI models for visual defect detection using deep learning.
• Integrating real-time inspection pipelines with industrial cameras and devices.
• Deploying and optimising AI-powered inspection systems for production environments.

Format of the Course

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

Course Customization Options

• To request customized 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 past experiences, thereby enhancing its capabilities based on the knowledge gained. These robots can collaborate with humans, working alongside them and learning from their behaviour. Moreover, they are capable of performing not only manual labour but also cognitive tasks. Beyond physical hardware, Smart Robots can exist purely as software-based applications on computers, operating without moving parts or direct physical interaction with the world.

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

The course is structured into 4 sections, each comprising three days of lectures, discussions, and hands-on robot development within a live laboratory environment. Each section concludes with a practical, hands-on project, allowing participants to practise and demonstrate their acquired knowledge.

The target hardware for this course will be simulated in 3D using simulation software. The ROS (Robot Operating System) open-source framework, along with C++ and Python, will be utilised for programming 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 in a robotic system
• Understand 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

Audience

• Developers
• Engineers

Format of the course

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

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