Autonomous Navigation & SLAM with ROS 2 Training Course

The integration of Artificial Intelligence (AI) with Robotics merges machine learning, control systems, and sensor fusion to engineer intelligent machines that can perceive, reason, and act with autonomy. By leveraging contemporary tools such as ROS 2, TensorFlow, and OpenCV, engineers are now empowered to design robots capable of navigating, planning, and interacting intelligently within real-world settings.

This instructor-led live training, available either online or on-site, targets intermediate-level engineers keen on developing, training, and deploying AI-driven robotic systems utilizing current open-source technologies and frameworks.

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

• Utilise Python and ROS 2 to construct and simulate robotic behaviours.
• Implement Kalman and Particle Filters for precise localization and tracking.
• Apply computer vision techniques via OpenCV for perception and object detection.
• Employ TensorFlow for motion prediction and learning-based control mechanisms.
• Integrate SLAM (Simultaneous Localization and Mapping) to enable autonomous navigation.
• Develop 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 sessions involving both simulated and real robotic environments.

Course Customization Options

To arrange a customized training session for this course, please contact us.

OpenCV is an open-source computer vision library that enables real-time image processing, while deep learning frameworks such as TensorFlow provide the tools for intelligent perception and decision-making in robotic systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level robotics engineers, computer vision practitioners, and machine learning engineers who wish to apply computer vision and deep learning techniques for robotic perception and autonomy.

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

• Implement computer vision pipelines using OpenCV.
• Integrate deep learning models for object detection and recognition.
• Use vision-based data for robotic control and navigation.
• Combine classical vision algorithms with deep neural networks.
• Deploy computer vision systems on embedded and robotic platforms.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using OpenCV and TensorFlow.
• Live-lab implementation on simulated or physical robotic systems.

Course Customization Options

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

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 intervention.

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

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

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

Audience

• Developers who wish to create their own bots

Course Format

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

Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, which reduces latency and power usage while enhancing autonomy and privacy within robotic systems.

This instructor-led, live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers looking 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 able to:

• Grasp the core principles of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models for speed, size, and energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level participants eager 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:

• Grasp the core principles of Human-Centric Physical AI and its real-world applications.
• Examine how collaborative robots contribute to enhancing workplace productivity.
• Recognize and tackle challenges associated with human-machine interactions.
• Develop workflows that optimize collaboration between humans and AI-driven systems.
• Foster a culture of innovation and adaptability in AI-integrated workplaces.

Human-Robot Interaction (HRI): Voice, Gesture & Collaborative Control is a practical course crafted to introduce participants to the design and implementation of intuitive interfaces for human–robot communication. This training merges theoretical knowledge, design principles, and programming practice to construct natural and responsive interaction systems utilizing speech, gestures, and shared control techniques. Participants will learn to integrate perception modules, develop multimodal input systems, and design robots that collaborate safely with humans.

This instructor-led live training, available online or onsite, targets beginner to intermediate-level participants aiming 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 foundational concepts and design principles of human–robot interaction.
• Develop voice-based control and response mechanisms for robots.
• Implement gesture recognition using computer vision techniques.
• Design collaborative control systems for safe and shared autonomy.
• Evaluate HRI systems based on usability, safety, and human factors.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding and design exercises.
• Practical experiments 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: Integrating ROS and PLC with Digital Twins is a practical course designed to bridge the gap between industrial automation and contemporary robotics frameworks. Participants will learn how to synchronize ROS-based robotic systems with PLCs, while exploring digital twin environments to simulate, monitor, and optimize production processes. The curriculum emphasizes interoperability, real-time control, and predictive analysis using digital replicas of physical systems.

This instructor-led, live training (available online or onsite) targets intermediate-level professionals seeking to develop practical skills in connecting ROS-controlled robots with PLC environments and implementing digital twins to enhance automation and manufacturing efficiency.

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

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

Course Format

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

Course Customization Options

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

This instructor-led live training in Nigeria (online or onsite) is designed for engineers who wish to explore the application of artificial intelligence to mechatronic systems.

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

• Gain a broad overview of artificial intelligence, machine learning, and computational intelligence.
• Understand the core concepts of neural networks and different learning methods.
• Effectively choose artificial intelligence approaches for solving real-life problems.
• Implement AI applications in 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 Nigeria (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

This instructor-led, live training in Nigeria (online or onsite) is aimed at intermediate-level participants who wish to enhance their skills in designing, programming, and deploying intelligent robotic systems for automation and beyond.

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

• Understand the principles of Physical AI and its applications in robotics and automation.
• Design and program intelligent robotic systems for dynamic environments.
• Implement AI models for autonomous decision-making in robots.
• Leverage simulation tools for robotic testing and optimization.
• Address challenges such as sensor fusion, real-time processing, and energy efficiency.

Practical Rapid Prototyping for Robotics with ROS 2 & Docker is a practical, hands-on course designed to assist developers in efficiently building, testing, and deploying robotic applications. Participants will learn to containerise robotics environments, integrate ROS 2 packages, and prototype modular robotic systems using Docker to ensure reproducibility and scalability. The course highlights agility, version control, and collaborative practices ideal for early-stage development and innovation teams.

This instructor-led, live training (available online or onsite) targets beginner to intermediate-level participants who want to accelerate their robotics development workflows using ROS 2 and Docker.

Upon completing this training, participants will be able to:

• Set up a ROS 2 development environment within Docker containers.
• Develop and test robotic prototypes in modular, reproducible setups.
• Use simulation tools to validate system behaviour before hardware deployment.
• Collaborate effectively using containerised robotics projects.
• Apply continuous integration and deployment concepts in robotics pipelines.

Course Format

• Interactive lectures and demonstrations.
• Hands-on exercises with ROS 2 and Docker environments.
• Mini-projects focused on real-world robotic applications.

Course Customisation Options

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

Reinforcement learning (RL) is a machine learning paradigm where agents learn to make decisions by interacting with an environment. In robotics, RL enables autonomous systems to develop adaptive control and decision-making capabilities through experience and feedback.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers, robotics researchers, and developers who wish to design, implement, and deploy reinforcement learning algorithms in robotic applications.

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

• Grasp the principles and mathematics of reinforcement learning.
• Implement RL algorithms such as Q-learning, DDPG, and PPO.
• Integrate RL with robotic simulation environments using OpenAI Gym and ROS 2.
• Train robots to perform complex tasks autonomously through trial and error.
• Optimize training performance using deep learning frameworks like PyTorch.

Format of the Course

• Interactive lecture and discussion.
• Hands-on implementation using Python, PyTorch, and OpenAI Gym.
• Practical exercises in simulated or physical robotic environments.

Course Customization Options

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

Robotic Manipulation and Grasping Using Deep Learning is an advanced programme that connects robotic control with contemporary machine learning techniques. Participants will investigate how deep learning can improve perception, motion planning, and dexterous grasping within robotic systems. Through theoretical study, simulation, and practical coding exercises, the programme guides learners from perception-based control to end-to-end policy learning for manipulation tasks.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals who wish to apply deep learning methods to achieve intelligent, adaptable, and precise robotic manipulation.

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

• Develop perception models for object recognition and pose estimation.
• Train neural networks for grasp detection and motion planning.
• Integrate deep learning modules with robotic controllers using ROS 2.
• Simulate and evaluate grasping and manipulation strategies in virtual environments.
• Deploy and optimise learned models on real or simulated robotic arms.

Course Format

• Expert-led lectures and algorithmic deep dives.
• Hands-on coding and simulation exercises.
• Project-based implementation and testing.

Course Customisation Options

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

Smart Robotics involves the integration of artificial intelligence into robotic systems to enhance perception, decision-making, and autonomous control.

This instructor-led live training (available online or onsite) targets advanced robotics engineers, systems integrators, and automation leads who wish to implement AI-driven perception, planning, and control in smart manufacturing environments.

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

• Understand and apply AI techniques for robotic perception and sensor fusion.
• Develop motion planning algorithms for collaborative and industrial robots.
• Deploy learning-based control strategies for real-time decision making.
• Integrate intelligent robotic systems into smart factory workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of 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.