Industrial Robotics Automation: ROS-PLC Integration & Digital Twins Training Course

The convergence of Artificial Intelligence (AI) and Robotics leverages machine learning, control systems, and sensor fusion to engineer intelligent machines that can perceive, reason, and act autonomously. By utilizing modern frameworks such as ROS 2, TensorFlow, and OpenCV, engineers are now equipped to design robotic systems that intelligently navigate, plan, and interact with complex real-world environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level engineers seeking to develop, train, and deploy AI-driven robotic systems using contemporary open-source technologies.

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

• Building and simulating robotic behaviors using Python and ROS 2.
• Implementing Kalman and Particle Filters for precise localization and tracking.
• Applying computer vision techniques via OpenCV for perception and object detection.
• Utilizing TensorFlow for motion prediction and learning-based control mechanisms.
• Integrating SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Developing reinforcement learning models to enhance robotic decision-making capabilities.

Format of the Course

• Interactive lectures and discussions.
• Hands-on implementation exercises using ROS 2 and Python.
• Practical application in both simulated and real-world robotic environments.

Course Customization Options

To arrange a customized training session for this course, please get in touch with us.

In this instructor-led, live training in Kenya (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.

The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.

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

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

• Understand the key concepts used in robotic technologies.
• Understand and manage the interaction between software and hardware in a robotic system.
• Understand and implement the software components that underpin robotics.
• Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
• Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
• Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
• Implement search algorithms and motion planning.
• Implement PID controls to regulate a robot's movement within an environment.
• Implement SLAM algorithms to enable a robot to map out an unknown environment.
• Extend a robot's ability to perform complex tasks through Deep Learning.
• Test and troubleshoot a robot in realistic scenarios.

ROS 2 (Robot Operating System 2) is an open-source framework designed to support the development of complex and scalable robotic applications.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers and developers who wish to implement autonomous navigation and SLAM (Simultaneous Localization and Mapping) using ROS 2.

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

• Set up and configure ROS 2 for autonomous navigation applications.
• Implement SLAM algorithms for mapping and localization.
• Integrate sensors such as LiDAR and cameras with ROS 2.
• Simulate and test autonomous navigation in Gazebo.
• Deploy navigation stacks on physical robots.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using ROS 2 tools and simulation environments.
• Live-lab implementation and testing on virtual or physical robots.

Course Customization Options

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

OpenCV serves as a powerful open-source library for computer vision, facilitating real-time image processing, while deep learning frameworks like TensorFlow equip robotic systems with the capabilities needed for intelligent perception and decision-making.

This instructor-led training, available online or on-site, is tailored for robotics engineers, computer vision specialists, and machine learning practitioners at an intermediate level. The program focuses on applying computer vision and deep learning methodologies to enhance robotic perception and autonomy.

Upon completing this training, participants will be equipped to:

• Build computer vision pipelines utilizing OpenCV.
• Incorporate deep learning models for effective object detection and recognition.
• Leverage visual data to guide robotic control and navigation.
• Synergize traditional vision algorithms with deep neural networks.
• Deploy computer vision solutions onto embedded devices and robotic platforms.

Training Format

• Engaging lectures paired with interactive discussions.
• Practical exercises using OpenCV and TensorFlow.
• Live lab sessions involving simulated or physical robotic systems.

Customization Options

• For tailored training arrangements, please reach out to us.

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

In this instructor-led live training, participants will gain foundational skills in bot development by building sample chatbots using industry-standard tools and frameworks.

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

• Identify the diverse applications and use cases of bots
• Comprehend the end-to-end bot development lifecycle
• Evaluate various tools and platforms suitable for bot construction
• Construct a sample chatbot for Facebook Messenger
• Develop a sample chatbot utilizing the Microsoft Bot Framework

Target Audience

• Developers eager to create their own bot solutions

Course Format

• A blend of lectures, group discussions, practical exercises, and extensive hands-on practice

Edge AI allows artificial intelligence models to execute directly on embedded or resource-constrained devices, thereby minimizing latency and power usage while enhancing autonomy and privacy within robotic systems.

This instructor-led, live training, available both online and onsite, targets intermediate-level embedded developers and robotics engineers eager to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

Upon completing this training, participants will be capable of:

• Gaining a solid understanding of TinyML and edge AI fundamentals for robotics.
• Converting and deploying AI models for on-device inference.
• Optimizing models to improve speed, reduce size, and enhance energy efficiency.
• Integrating edge AI systems into robotic control architectures.
• Evaluating performance and accuracy in real-world scenarios.

Format of the Course

• Interactive lectures and discussions.
• Hands-on practice utilizing TinyML and edge AI toolchains.
• Practical exercises conducted on embedded and robotic hardware platforms.

Course Customization Options

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

This instructor-led, live training in Kenya (online or onsite) is aimed at intermediate-level participants who wish to explore the role of collaborative robots (cobots) and other human-centric AI systems in modern workplaces.

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

• Understand the principles of Human-Centric Physical AI and its applications.
• Explore the role of collaborative robots in enhancing workplace productivity.
• Identify and address challenges in human-machine interactions.
• Design workflows that optimize collaboration between humans and AI-driven systems.
• Promote a culture of innovation and adaptability in AI-integrated workplaces.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course created to familiarize participants with the design and deployment of intuitive interfaces for communication between humans and robots. The training integrates theoretical knowledge, design principles, and programming practice to construct natural and responsive interaction systems utilizing speech, gestures, and shared control methods. Participants will acquire skills in integrating perception modules, creating multimodal input systems, and designing robots that can safely collaborate with humans.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants who aim to design and implement human–robot interaction systems that improve usability, safety, and overall user experience.

Upon completing this training, participants will be able to:

• Grasp the foundations and design principles of human–robot interaction.
• Create voice-based control and response mechanisms for robots.
• Implement gesture recognition using computer vision techniques.
• Design collaborative control systems to ensure safe and shared autonomy.
• Evaluate HRI systems based on usability, safety, and human factors.

Format of the Course

• Interactive lectures and demonstrations.
• Hands-on coding and design exercises.
• Practical experiments conducted in simulation or real robotic environments.

Course Customization Options

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

This instructor-led live training in Kenya (online or onsite) is designed for engineers who want to explore how artificial intelligence can be applied to mechatronic systems.

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

• Obtain an overview of artificial intelligence, machine learning, and computational intelligence.
• Grasp the concepts of neural networks and various learning methods.
• Select the most effective AI approaches for addressing real-world problems.
• Implement AI applications within mechatronic engineering.

Multi-Robot Systems and Swarm Intelligence is an advanced training course that explores the design, coordination, and control of robotic teams inspired by biological swarm behaviors. Participants will learn how to model interactions, implement distributed decision-making, and optimize collaboration across multiple agents. The course combines theory with hands-on simulation to prepare learners for applications in logistics, defense, search and rescue, and autonomous exploration.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to design, simulate, and implement multi-robot and swarm-based systems using open-source frameworks and algorithms.

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

• Understand the principles and dynamics of swarm intelligence and cooperative robotics.
• Design communication and coordination strategies for multi-robot systems.
• Implement distributed decision-making and consensus algorithms.
• Simulate collective behaviors such as formation control, flocking, and coverage.
• Apply swarm-based techniques to real-world scenarios and optimization problems.

Format of the Course

• Advanced lectures with algorithmic deep dives.
• Hands-on coding and simulation in ROS 2 and Gazebo.
• Collaborative project applying swarm intelligence principles.

Course Customization Options

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

This instructor-led, live training in Kenya (online or onsite) is designed for advanced robotics engineers and AI researchers eager to harness Multimodal AI to integrate diverse sensory data, thereby creating robots that are more autonomous, efficient, and capable of seeing, hearing, and touching.

Upon completing this training, participants will be equipped to:

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Build robots capable of executing complex tasks in dynamic environments.
• Tackle challenges associated with real-time data processing and actuation.

This instructor-led training, delivered in Kenya (online or on-site), is designed for intermediate-level automation engineers and control system designers aiming to implement motion control solutions using Omron PLCs.

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

• Understand fundamental motion control concepts and their applications.
• Configure motion hardware and software in Sysmac Studio.
• Program and optimize single-axis and multi-axis motion control.
• Implement coordinated motion strategies, including interpolation and synchronization.

This instructor-led, live training in Kenya (online or onsite) is aimed at automation engineers and control system designers who wish to configure, program, and commission Omron Sysmac systems covering NJ/NX controllers, EtherCAT networking, G5/1S/1SA servo drives, NA Series HMI, and NX safety hardware.

This course provides students with a foundational understanding of Programmable Logic Controllers (PLC). Following an exploration of core PLC principles, learners will study and apply essential Ladder Diagram instructions to real-world Industrial Automation scenarios. Target Audience: Electrical Specialists, Mechanical Engineers, and Programmers interested in Industrial Automation.

Smart Robotics involves integrating artificial intelligence into robotic systems to enhance perception, decision-making capabilities, and autonomous control.

This instructor-led live training, available either online or onsite, is designed for advanced robotics engineers, systems integrators, and automation leads who aim to implement AI-driven perception, planning, and control within smart manufacturing settings.

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

• Grasp and apply AI techniques for robotic perception and sensor fusion.
• Create motion planning algorithms for both collaborative and industrial robots.
• Implement learning-based control strategies to facilitate real-time decision-making.
• Seamlessly integrate intelligent robotic systems into smart factory workflows.

Course Format

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

Course Customization Options

• To request tailored training for this course, please reach out to us to make the necessary arrangements.