In an era marked by technological advancements and a growing need for efficiency, autonomous mobile robots have emerged as indispensable assets in industrial production settings. These robots, equipped with cutting-edge technology and artificial intelligence, are revolutionizing the way industries operate.

Our course on Autonomous Mobile Robots is designed to immerse you in this exciting field, providing you with the knowledge and skills to harness the full potential of these robotic marvels. Throughout this course, you will embark on a journey that explores the intricate details of programming, operating, and optimizing autonomous mobile robots, all within the dynamic landscape of industrial production.

As you delve into this immersive experience, you will encounter a rich tapestry of topics, including robot localization and navigation, perception, mapping, control systems, and much more. You will gain firsthand experience in handling data from integrated laser scanners, 3D cameras, accelerometers, gyroscopes, and radar systems, all essential components for informed decision-making in real-world scenarios.

In our quest for mastery, we will explore a plethora of artificial intelligence algorithms, from automatic learning and reinforced learning to fuzzy logic. Additionally, we will venture into the realm of automatic mapping of the environment, employing optimization techniques and agent-based modeling to enhance the design of these autonomous robots. Deep learning models, including those based on neural networks, will be at your disposal for environment recognition and problem-solving.

Furthermore, we will delve into the role of autonomous mobile robots in the Industry 4.0 landscape, where they are pivotal in implementing advanced production concepts such as agile and lean production. The potential for innovation and increased efficiency is boundless, and we are excited to guide you on this transformative journey.

Join us in unlocking the secrets of autonomous mobile robots and discover how these technological marvels are reshaping the future of industrial production. Get ready to equip yourself with the tools and knowledge needed to drive innovation and efficiency in this ever-evolving field. Stay tuned for further details about this course, as we prepare to embark on this exciting adventure together!

Total Hours

This course unit covers 75 hours, from which 14 hours lectures, 14 hours lab work, and 47 hours individual study and work.

General Objective

The overarching goal of this course is to enable students to comprehend the concept of autonomous mobile robots and their application in industrial production. By the course’s end, students will possess the knowledge and practical skills required to design, program, and operate autonomous mobile robots effectively.

Specific Objectives / Learning Outcomes

Throughout this course on Autonomous Mobile Robots, students will achieve the following specific objectives and learning outcomes:

  •  Develop a clear understanding of the fundamental concepts and principles governing autonomous mobile robots, including their capabilities, applications, benefits, and limitations.
  • Acquire practical skills in programming autonomous mobile robots, encompassing navigation algorithms, sensor integration, and real-time control, to enable them to operate autonomously in various environments.
  • Gain expertise in integrating and configuring a diverse range of sensors, such as laser scanners, 3D cameras, accelerometers, and gyroscopes, for accurate data acquisition and perception in real-world scenarios.
  • Master the art of robot localization and navigation in dynamic environments, utilizing techniques such as SLAM (Simultaneous Localization and Mapping) and path planning algorithms.
  • Develop the ability to design and implement control systems that ensure precise and efficient robot movements and interactions with the environment.
  • Apply artificial intelligence and machine learning algorithms to enhance robot decision-making, adaptability, and problem-solving capabilities in complex situations.
  • Identify and address the challenges commonly encountered in autonomous robotics, such as sensor uncertainties, dynamic obstacle avoidance, and robust system performance.
  • Apply optimization techniques, including evolutionary algorithms, to fine-tune robot behavior and performance, ensuring they are optimized for specific industrial tasks.
  • Understand the pivotal role of autonomous mobile robots in Industry 4.0 and apply this knowledge to implement advanced production concepts such as agile and lean production.
  • Develop the ability to innovate in the application of autonomous robots, leveraging them to improve efficiency, productivity, and quality in industrial production settings.
  • Learn how to systematically evaluate and validate the performance of autonomous mobile robots, ensuring their reliability and effectiveness in real-world applications.
  • Enhance communication skills to articulate and present robot design and implementation strategies effectively, both to technical and non-technical stakeholders.
  • Foster teamwork skills by actively participating in group projects, where students collaborate to design, build, and evaluate autonomous mobile robots for industrial production scenarios.
Professional Competencies

Upon successful completion of this course on Autonomous Mobile Robots, students will acquire a range of professional competencies vital for a career in the field of autonomous robotics and industrial production. These competencies include:

  • A solid understanding of the principles and intricacies of autonomous mobile robots, encompassing their hardware, software, and operational concepts.
  • Proficiency in integrating a diverse array of sensors, including laser scanners, cameras, and IMUs, and harnessing their data for informed decision-making.
  • Competence in robot localization, path planning, and obstacle avoidance, enabling robots to navigate safely and efficiently in complex environments.
  • The ability to program robots using various programming languages and environments, tailoring their behavior to specific industrial tasks.
  • Mastery of artificial intelligence and machine learning techniques for enhancing robot adaptability, perception, and autonomous decision-making.
  • Expertise in seamlessly integrating robotic systems into existing industrial workflows, including communication protocols and hardware interfaces.
  • The capability to employ optimization algorithms, including evolutionary and reinforcement learning, for fine-tuning robot behavior and performance.
  • The ability to identify, analyze, and address challenges and limitations encountered in the deployment of autonomous mobile robots.
  • An understanding of the role of autonomous robots in Industry 4.0 and the capacity to apply this knowledge to enhance production processes.
  • Awareness of the ethical and legal considerations related to autonomous mobile robots in industrial settings, including data privacy and cybersecurity.
  • Proficiency in systematically evaluating and validating the performance of autonomous robots to ensure their reliability and effectiveness.
  • Strong communication skills to convey technical concepts and present robot design and implementation strategies to diverse stakeholders.
  • The ability to work effectively within multidisciplinary teams, collaborating with engineers, technicians, and production managers to achieve common objectives.
  • The capacity to innovate and optimize industrial processes through the strategic deployment of autonomous mobile robots.
  • Utilization of data analytics to optimize robot deployment, enhance performance, and develop predictive maintenance strategies.
  • A commitment to staying updated with emerging technologies and methodologies in the field of autonomous robotics and industrial production.
Cross Competencies

The cross-competencies that students can develop while taking this course on Autonomous Mobile Robots are:

  • Enhance the ability to critically analyze complex situations and make informed decisions when designing, programming, and operating autonomous mobile robots in industrial settings.

  • Develop problem-solving skills applicable not only to robotics but also to broader industrial and engineering challenges, enabling students to approach complex issues methodically.
  • Gain experience in managing robotics projects, including planning, resource allocation, and time management, which can be applied to various roles within an organization.
  •  Improve the capacity to work effectively with professionals from diverse backgrounds, fostering collaboration between robotics experts, engineers, technicians, and production teams.
  • Enhance communication skills to convey technical concepts clearly and adapt messages for different audiences, including both technical and non-technical stakeholders.
  • Develop an awareness of the ethical implications of robotics technology, including considerations related to privacy, safety, and societal impacts, and the ability to make ethically sound decisions.
  • Cultivate adaptability and flexibility in dealing with evolving technologies and dynamic industrial environments, ensuring the ability to embrace change and innovation.
  • Strengthen skills in data interpretation and data-driven decision-making, which can be applied beyond robotics to various data-centric roles in industrial production.
  • Foster an innovation-oriented mindset, promoting creative thinking and the ability to identify opportunities for applying robotics solutions to enhance industrial processes.
  • Develop leadership qualities that can be applied in roles requiring the management and coordination of robotics initiatives within an organization.
  • Gain an understanding of the global landscape of robotics and industrial production, considering international standards, best practices, and emerging trends.
  • Acquire skills in identifying and mitigating risks associated with autonomous mobile robots, enhancing the ability to ensure the safety and reliability of robotic systems.

Alignment to Social and Economic Expectations

This course on Autonomous Mobile Robots aligns closely with both social and economic expectations, contributing to societal progress and economic growth in several ways:

  • Autonomous mobile robots are at the forefront of technological innovation. By equipping students with the skills and knowledge needed to work with these robots, the course prepares them to be active contributors to technological advancements that drive economic growth.
  • As industries increasingly adopt autonomous robots to improve efficiency, the course ensures that students are well-prepared to meet the demands of the job market, contributing to a skilled workforce capable of managing and optimizing robotic systems.
  • By optimizing production processes through the deployment of autonomous robots, graduates of this course can help industries reduce costs, increase productivity, and remain competitive on a global scale, contributing to economic stability.
  • Autonomous robots can enhance workplace safety and reduce environmental impact. Graduates who prioritize safety and sustainability in their work align with societal expectations for responsible technological implementation.
  • The growth of the robotics industry creates job opportunities not only in the design and operation of robots but also in maintenance, support, and supervision roles. This course prepares students for diverse career paths, contributing to job creation.
  • By emphasizing the ethical implications of autonomous robots, the course ensures that graduates are equipped to make ethically sound decisions when deploying these technologies, meeting societal expectations for responsible innovation.
  • Graduates with expertise in autonomous mobile robots contribute to the global competitiveness of industries. Their skills enable companies to compete effectively in international markets, supporting economic growth.
  • The course encourages interdisciplinary collaboration, fostering a work culture where professionals from various backgrounds work together effectively, aligning with the social expectation for diverse and inclusive workplaces.
  • In an era where data is a valuable resource, students trained in data interpretation and data-driven decision-making contribute to informed and efficient industrial practices, aligning with economic expectations for data-driven enterprises.
  • Graduates who embrace an innovation-oriented mindset can become catalysts for entrepreneurship and the growth of innovation ecosystems, driving economic development in their regions.

Assessment Methods

In our course on Autonomous Mobile Robots, we employ a variety of assessment methods to comprehensively evaluate students’ understanding and proficiency in both the theoretical and practical aspects of autonomous robotics.

For the theoretical lectures component, the following assessment methods are utilized:

  1. Quizzes: Regular in-class or online quizzes will gauge students’ comprehension of fundamental concepts, theories, and principles presented during lectures.
  2. Written Assignments: Individual assignments will challenge students to apply their theoretical knowledge to real-world scenarios, fostering critical thinking and problem-solving skills.
  3. Midterm and Final Exams: These comprehensive exams will assess students’ overall understanding of the course material, including multiple-choice questions, short answer questions, and essay questions.

For the practical laboratory component, the following assessment methods are employed:

  1. Lab Reports: Students are required to submit detailed lab reports documenting their experiments, results, and analytical insights. Evaluation criteria include the quality of writing, methodology, results, and analysis.
  2. Oral Presentations: Students must present their lab work to the class, with assessments based on presentation skills, content clarity, and their ability to engage with the audience.

Assessment Criteria

In the lectures portion of our course on Autonomous Mobile Robots, the assessment criteria encompass various dimensions:

  1. Knowledge and Understanding: Assessing students’ ability to comprehend and apply the core concepts, theories, and principles of autonomous mobile robots in industrial production.
  2. Analytical and Problem-Solving Skills: Evaluating students’ capacity to analyze complex problems, assess different solutions, and make informed decisions in the context of autonomous robotics.
  3. Communication Skills: Assessing students’ aptitude in effectively conveying ideas, designs, and solutions in a clear, concise, and engaging manner.
  4. Teamwork and Collaboration Skills: Evaluating students’ collaborative abilities and their capacity to work effectively within teams to achieve common objectives.
  5. Application of Technology: Gauging students’ proficiency in applying relevant technologies, tools, and software essential for the field of autonomous mobile robots.

For the laboratory work component, the assessment criteria include:

  1. Technical Skills: Evaluating students’ competence in applying technical skills and knowledge acquired during the course to develop practical solutions in the realm of autonomous mobile robots.
  2. Quality of Work: Assessing students’ ability to produce high-quality work that adheres to established standards and requirements.
  3. Creativity and Innovation: Gauging students’ capacity for creative thinking and the application of innovative solutions within the context of autonomous mobile robots.
  4. Attention to Detail: Evaluating students’ attention to detail to ensure the accuracy, completeness, and thorough documentation of autonomous mobile robot solutions.
  5. Time Management: Assessing students’ time management skills and their ability to deliver completed lab work within specified timeframes.

Quantitative Performance Indicators

To assess the minimum level of performance (marked as 5 on a scale from 1 to 10), quantitative performance indicators are utilized for lectures and lab works:

For Lectures:

  • Attendance and Participation in Class Discussions: Students are expected to attend at least 80% of lectures and actively participate in class discussions.
  • Homework and Quizzes: Students should complete all homework assignments and quizzes with a minimum score of 60%.
  • Midterm Exam: A minimum score of 50% on the midterm exam is expected.

For Lab Works:

  • Lab Attendance and Participation: Students are required to attend and actively participate in all scheduled lab sessions.
  • Lab Reports: Submission of all lab reports on time, with a minimum score of 60% for each report.
  • Lab Assignments: Completion of all lab assignments with a minimum score of 60%.
  • Lab Exams: Achievement of a minimum score of 50% on lab exams.

For the Final Exam:

  • Completion of a Minimum Number of Lecture-Related Questions Correctly: 70% of the total questions.
  • Demonstration of Understanding of Basic Concepts and Theories: A minimum score of 50% on multiple-choice questions or short-answer questions.
  • Analysis of Real-Life Case Studies: A minimum score of 50% on case study analysis questions.
  • Knowledge of Technologies, Tools, and Methodologies: A minimum score of 50% on matching or labeling questions.
  • Application of Concepts and Theories to Practical Problems: A minimum score of 50% on problem-solving questions.
  • Critical Evaluation of Benefits and Challenges: A minimum score of 50% on essay questions.
  • Evidence of Application of Learned Concepts and Theories: Demonstrated through correctly answered application-based questions.
  • Display of Critical Thinking Skills: Evidenced by correct answers to questions requiring analysis and synthesis of information.
  • Overall Exam Performance: Evaluated as a percentage of the total exam score, with a minimum score of 50% or above considered a passing mark (5).

Unit 1: Comprehensive Introduction to ROS: Exploring ROS 1 and ROS 2 Systems (2 hours)

  • Introduction to ROS (Robot Operating System)
  • Understanding the differences between ROS 1 and ROS 2
  • Key features and advantages of ROS 2
  • Use cases and applications of ROS in the robotics industry
  • Practical demonstrations and hands-on exploration of ROS systems

Unit 2: URDF and XACRO File Development with SolidWorks and Gazebo (2 hours)

  • Introduction to URDF (Unified Robot Description Format) and XACRO files
  • Integration of SolidWorks models into the Gazebo simulation environment
  • Creating and modifying URDF files for robot modeling
  • Practical exercises and demonstrations using SolidWorks and Gazebo

Unit 3: Sensory Science and Locomotion Systems: Practical Applications in Gazebo (2 hours)

  • Understanding sensors in robotics
  • Types of locomotion systems and their applications
  • Hands-on experience with sensor data in Gazebo
  • Simulation of different locomotion mechanisms in Gazebo
  • Case studies demonstrating sensor integration and locomotion control

Unit 4: Advanced Navigation and Obstacle Detection: Techniques and Parameterization (2 hours)

  • Advanced navigation strategies in autonomous robots
  • In-depth exploration of move base functionalities
  • Parameterization and configuration for fine-tuning robot behavior
  • Techniques and algorithms for effective obstacle detection
  • Practical exercises on navigation and obstacle avoidance

Unit 5: Mapping Systems Explored: SLAM and V-SLAM Technologies (2 hours)

  • Simultaneous Localization and Mapping (SLAM) principles
  • Visual SLAM (V-SLAM) and its advantages
  • Real-world applications of SLAM and V-SLAM
  • Hands-on experience with SLAM and V-SLAM technologies
  • Mapping and localization in dynamic environments

Unit 6: Artificial Intelligence in Robotics: Pointcloud and Voice Mode Applications (2 hours)

  • Integration of Artificial Intelligence in robotics
  • Pointcloud analysis and its significance
  • Implementing voice modes for human-robot interaction
  • Real-world AI applications in autonomous robots
  • Practical demonstrations of AI-driven robotics

Unit 7: Industrial Robotics Control: PC, App Control, Rosbridge, and Foxgloves (2 hours)

  • Advanced control systems in industrial robotics
  • Utilizing industrial PCs for monitoring and control
  • App-based control interfaces for robots
  • Introduction to Rosbridge for communication
  • Visualization and monitoring with Foxgloves
  • Case studies showcasing industrial robotics control systems
Lab Work

Unit 1: ROS Basics: Terminal Commands and Installation (2 hours)

  • Introduction to ROS terminal commands
  • Installation of ROS 1 with packet creation
  • Running example ROS programs
  • Hands-on practice with ROS terminal commands

Unit 2: URDF and XACRO File Creation and SolidWorks Integration in Gazebo (2 hours)

  • Creating URDF and XACRO files for robot modeling
  • Integrating SolidWorks models into Gazebo
  • Modifying URDF and XACRO files
  • Practical exercises with URDF and XACRO files in Gazebo

Unit 3: Navigation and Obstacle Detection with Move_Base and NAV Stack Configuration (2 hours)

  • Configuring move_base and NAV Stack for navigation
  • Parameterization of move_base for robot control
  • Implementing obstacle detection strategies
  • Hands-on exercises involving navigation and obstacle avoidance

Unit 4: Mapping Systems: Understanding SLAM and V-SLAM (2 hours)

  • Simultaneous Localization and Mapping (SLAM) principles
  • Visual SLAM (V-SLAM) concepts and applications
  • Practical experience with SLAM and V-SLAM technologies
  • Mapping and localization in dynamic environments

Unit 5: AI Applications: Pointcloud Analysis and Voice Module Integration (2 hours)

  • Pointcloud analysis and its role in robotics
  • Integration of voice modules for human-robot interaction
  • Hands-on exercises in implementing AI-driven applications
  • Real-world AI applications in autonomous robots

Unit 6: Advanced Control with Industrial PC, App Control, and Rosbridge (2 hours)

  • Utilizing industrial PCs for advanced robot control
  • App-based control interfaces and their benefits
  • Introduction to Rosbridge for communication
  • Visualization and monitoring using Foxglove Studio
  • Practical sessions on advanced robot control systems

Unit 7: Comprehensive Review and Practical Applications (2 hours)

  • Reviewing key concepts and topics covered in the course
  • Discussion of practical applications and real-world scenarios
  • Solving complex problems and challenges using acquired knowledge
  • Hands-on exercises to reinforce practical skills
  • Q&A session for clarifications and additional insights
Supporting Infrastructure

To run the activity for this course unit, students will have the possibility to work in our labs with

– an autonomous mobile platform Agilex having:

  • Open source operating system such as ROS 1 or ROS 2
  • Gazebo integration capability
  • Applicability for artificial intelligence
  • 4 omnidirectional wheels (SLAM, V-SLAM)
  • Mapping system, navigation, obstacle detection, LIDAR
  • Includes stereo camera and voice module
  • Includes industrial PC, APP control, monitor

– a mobile platform Unitree having:

  • Python or equivalent programming API
  • Graphic processing API
  • Human recognition
  • Minimum 4G communication
  • Limb sensors
  • Multimodal interface