Computer Vision for Robotics: Perception with OpenCV & Deep Learning 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.

Azure Bot Service unites the strengths of the Microsoft Bot Framework and Azure Functions, delivering a robust platform for rapidly constructing smart bots.

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

Upon completing the training, participants will be able to:

Grasp the fundamental concepts underlying smart bots.

Construct smart bots using cloud-based applications.

Acquire practical insights into the Microsoft Bot Framework, the Bot Builder SDK, and Azure Bot Service.

Implement proven bot design patterns in real-world situations.

Build and deploy their first smart bot using Microsoft Azure.

Target Audience

This course is tailored for developers, enthusiasts, 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 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.

Industrial Robotics Automation: ROS-PLC Integration & Digital Twins is a practical course designed to bridge the gap between industrial automation and modern robotics frameworks. Participants will acquire the skills to integrate ROS-based robotic systems with PLCs for synchronized operations, while exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum places a strong emphasis on interoperability, real-time control, and predictive analysis through the use of digital replicas of physical systems.

This instructor-led live training, available either online or onsite, targets intermediate-level professionals seeking to develop practical competencies in linking ROS-controlled robots with PLC environments and implementing digital twins for automation and manufacturing optimization.

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

• Comprehend the communication protocols facilitating interaction between ROS and PLC systems.
• Execute real-time data exchange mechanisms between robots and industrial controllers.
• Create digital twins for monitoring, testing, and simulating processes.
• Integrate sensors, actuators, and robotic manipulators into industrial workflows.
• Design and validate industrial automation systems using hybrid simulation environments.

Course Format

• Interactive lectures accompanied by architecture walkthroughs.
• Practical exercises focused on integrating ROS and PLC systems.
• Implementation of simulation and digital twin projects.

Customization Options for the Course

• To request a customized training session for this course, please get in touch with us to make arrangements.

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, live training in Kenya (online or onsite) is designed for intermediate-level participants looking to strengthen their skills in designing, programming, and deploying intelligent robotic systems for automation and other applications.

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

• Grasp the core principles of Physical AI and its uses in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models that enable autonomous decision-making in robots.
• Utilize simulation tools for robotic testing and optimization.
• Address challenges like sensor fusion, real-time processing, and energy efficiency.

A smart robot is an Artificial Intelligence (AI) system capable of learning from its surroundings and past experiences, enhancing its capabilities through that knowledge. These robots can collaborate with humans, working alongside them and learning from their actions. Furthermore, they are equipped to handle not only manual labour but also cognitive tasks. Beyond physical machines, smart robots can also exist purely as software, residing within a computer as an application without moving parts or physical interaction with the real world.

In this instructor-led live training, participants will explore the various technologies, frameworks, and techniques required to program different types of mechanical smart robots, 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 lab environment. Each section concludes with a practical hands-on project, allowing participants to practice and demonstrate their acquired knowledge.

The target hardware for this course will be simulated in 3D using simulation software. The open-source ROS (Robot Operating System) framework, along with C++ and Python, will be used 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 support 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 blend of lectures, discussions, exercises, and extensive hands-on practice

Note

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

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.