Collaborative Robotic Systems

Syllabus

Introduction

In a time where cutting-edge technology and peak efficiency are the driving forces, the realm of industrial production is witnessing a significant shift, propelled by the advent of Collaborative Robotic Systems. These advanced systems, epitomizing a seamless fusion of artificial intelligence and state-of-the-art robotics, are reshaping the contours of industrial automation and production.

Our course on Collaborative Robotic Systems is meticulously crafted to immerse you deeply into the world of these intelligent machines. As a part of this course, you will be embarked on an enlightening journey that encompasses the nuanced aspects of designing, programming, and implementing collaborative robots (cobots) in modern industrial environments.

As you navigate through this course, you will unravel a diverse array of topics that are integral to understanding and leveraging collaborative robotic systems. These include the intricate mechanics of human-robot collaboration, the principles of AI that empower these robots, and the complex algorithms that enable them to operate autonomously and safely alongside human counterparts.

You will gain hands-on experience with state-of-the-art sensors, vision systems, and gripping technologies, which are the eyes and hands of cobots in a production setting. Moreover, the course will guide you through the nuances of machine learning, including supervised, unsupervised, and reinforcement learning, which are crucial for the adaptive and predictive capabilities of collaborative robots.

In addition, this course delves into the strategic implementation of cobots in the framework of Industry 4.0, highlighting their role in enhancing flexibility, efficiency, and customization in production processes. We will explore cutting-edge case studies and real-world applications of cobots across various sectors, from automotive to electronics, offering a panoramic view of their transformative impact.

This course not only equips you with technical knowledge but also encourages you to contemplate the ethical and safety considerations inherent in deploying robots in human-centric environments. You will learn about the standards and best practices for ensuring safe and harmonious human-robot interactions.

Total Hours

This course unit covers 100 hours, from which 14 hours lectures, 28 hours lab work, and 58 hours individual study and work.

General Objective

The general objective of this course module is to foster a comprehensive understanding of the major concepts, usage, and application development within the framework of collaborative robots. This entails a deep dive into the theoretical underpinnings that govern the design and functionality of cobots, an exploration of their practical applications across various industrial sectors, and an immersion into the developmental processes that enable their integration and optimization in real-world settings. By mastering these aspects, students will be equipped with the knowledge and skills necessary to effectively implement and innovate with collaborative robotic systems in the dynamic landscape of industrial automation.

Specific Objectives / Learning Outcomes

Building upon the general objective of understanding the major concepts, usage, and application development within the framework of collaborative robots, this course module is structured around the following specific objectives:

Development of Specific Algorithms for Problem Solving: Students will learn to develop and implement algorithms tailored to address unique challenges in the field of collaborative robotics. This includes algorithmic problem solving for enhanced robot functionality, efficiency, and task-specific operations.
Basic Skills for Programming Different Models of Collaborative Robots: The course aims to impart foundational programming skills applicable to various models of collaborative robots. Students will gain hands-on experience in writing and debugging code, understanding the nuances of different cobot architectures and software environments.
Development of Specific Algorithms in MATLAB for Interactive Control of Collaborative Robots: Focusing on MATLAB, a key tool in robotic programming, students will develop algorithms for the interactive control of collaborative robots. This includes creating MATLAB scripts and functions for real-time robot manipulation and task execution.
Understanding the Specific Features and Properties of Collaborative Robots: This objective aims to provide students with an in-depth understanding of the unique features and properties of collaborative robots, such as safety mechanisms, sensory feedback systems, and human-robot interaction capabilities.
Implementation of Intelligent, Adaptive, and Interactive Algorithms on Different Collaborative Robots: Students will be tasked with implementing algorithms that enable cobots to exhibit intelligent, adaptive, and interactive behaviors. This involves programming robots to learn from their environment, adapt to new tasks, and interact seamlessly with human operators.
Applications Development Based on the Particular Features of Collaborative Robots: The course will guide students through the process of developing applications that leverage the specific capabilities and features of collaborative robots. This will involve creating custom solutions for industries where cobots can significantly enhance efficiency and productivity.

Through these specific objectives, students will acquire a comprehensive skill set that prepares them to innovate and excel in the field of collaborative robotics, with a strong emphasis on practical application and real-world problem solving.

Professional Competencies

Upon successful completion of this course, students will have developed a range of professional competencies that are critical in the field of collaborative robotics. These competencies will enable them to effectively engage with and contribute to various aspects of this rapidly evolving industry. The competencies include:

Comprehensive Understanding of Collaborative Robot Concepts: Students will have a thorough understanding of the specific concepts underlying collaborative robots, including their design, operation, and integration into various industrial processes.
Proficiency in Developing Applications with KUKA iiwa LBR: Students will gain proficiency in developing basic applications using the KUKA iiwa LBR collaborative robot, demonstrating an ability to work with its unique features and programming environment.
Skill in Application Development with UR5e Robot: The course will equip students with the skills to develop basic applications using the UR5e collaborative robot, highlighting their ability to adapt to different robotic platforms and their specific programming languages and tools.
Capability in ABB YUMI Collaborative Robot Applications: Students will acquire the capability to develop basic applications with the ABB YUMI collaborative robot, showcasing their versatility in handling robots designed for intricate and precise collaborative tasks.
Expertise in Developing Applications with MAiRA Collaborative Robot: The course will enable students to develop basic applications using the MAiRA collaborative robot, emphasizing their competence in engaging with emerging and advanced cobot technologies.
Integration of Multiple Control Solutions for Collaborative Robots: Students will learn to develop applications that integrate multiple control solutions, demonstrating their ability to create complex and sophisticated cobot systems that can operate efficiently in diverse environments.
Understanding of Intelligent and Interactive Algorithm Implementation: Finally, students will understand how to implement intelligent and interactive algorithms in applications developed with collaborative robots, showcasing their ability to enhance the autonomy and adaptability of cobots in various industrial applications.

These competencies will equip students not only with the technical know-how but also with the innovative mindset required to address challenges and seize opportunities in the field of collaborative robotics.

Cross Competencies

Upon completion of this course, students will have cultivated a set of cross-competencies that extend beyond technical skills, enriching their professional and personal development. These competencies are essential in today’s interdisciplinary and rapidly evolving work environments. They include:

Teamwork: Students will learn to collaborate effectively with peers, understanding the importance of diverse perspectives and skills in solving complex problems. They will gain experience in working in multi-disciplinary teams, enhancing their ability to communicate, delegate, and integrate different approaches for a common goal.
Efficient Use of Information Resources and Communication Solutions: The course will enhance students’ ability to utilize a range of information resources and communication tools proficiently. This includes using specific software applications like MATLAB, accessing databases, and leveraging online lectures. Students will also develop proficiency in communicating complex technical information, too.
Basic Values and Professional Ethics of Engineers: Students will be ingrained with the core values and professional ethics of engineering, including integrity, accountability, and respect for resources. They will understand the importance of responsible decision-making in achieving technical tasks and assignments, and the impact of their work on society and the environment.
Complex Tasks Management: The course will equip students with skills to manage complex tasks effectively. This involves identifying main objectives, creating a structured plan with a clear roadmap, establishing milestones, and defining deliverables. They will learn to approach tasks methodically, enhancing their project management and organizational skills.
Conduct Basic Research Activities: Students will be trained to conduct basic research activities, which include formulating research questions, conducting literature reviews, designing experiments, collecting and analyzing data, and presenting findings. This skill is vital for continuous learning and contributing to the advancement of the field.
Continuous Education: The course will foster an attitude of continuous learning and self-improvement. Students will recognize the importance of staying updated with the latest developments in their field, adapting to new technologies and methodologies, and pursuing further education and professional development opportunities.

These cross-competencies are designed to prepare students not just as skilled professionals in collaborative robotics, but also as adaptable, ethical, and collaborative members of the workforce, capable of contributing significantly to their chosen fields and the broader community.

Alignment to Social and Economic Expectations

This course is meticulously designed to align the skills and knowledge acquired by graduates with the current social and economic expectations prevalent in the field of advanced manufacturing and automation. The focus on conceptualizing and developing control programs and applications for collaborative robots directly responds to the growing demand for professionals skilled in the integration of robotics with artificial intelligence in various industrial sectors.

Addressing Industry Needs: By enabling graduates to understand and create programs for collaborative robots, the course directly addresses the needs of industries looking to enhance efficiency, precision, and safety in production processes. The specific applications in mechanics, robot modeling, optimizations, intelligent algorithms, haptic feedback control, and voice control are not only relevant but also critical in meeting the sophisticated demands of modern manufacturing and service industries.
Interdisciplinary Approach: The inclusion of related fields such as mechanics, robot modeling, and intelligent algorithms ensures that graduates are not only proficient in robotic systems but also have a broad understanding of the interdisciplinary nature of modern automation. This approach is aligned with the economic trend towards interdisciplinary innovation and the development of holistic solutions in industrial automation.
Preparation for Dynamic Industry Demands: The course equips students with techniques and tools to rapidly integrate collaborative robots into dynamic applications. This skill is particularly valuable in today’s fast-paced industrial environment where flexibility and the ability to quickly adapt to new technologies are highly prized. The integration of artificial intelligence elements in these applications is a direct response to the economic shift towards smart manufacturing and Industry 4.0.
Social Impact and Relevance: The skills learned in this course have significant social implications, as the implementation of collaborative robots can lead to safer work environments and can free human workers from repetitive and hazardous tasks. This aligns with the broader social expectation of creating work environments that prioritize human welfare and ergonomics.
Future-Oriented Skills: The course is forward-looking, preparing students for the future landscape of work where automation, AI, and robotics will play an even more central role. Graduates will be well-positioned to contribute to and lead in the next wave of technological innovation, ensuring economic competitiveness and addressing the evolving needs of a digitalized and automated world.

Evaluation

Assessment Methods

In our course on Collaborative Robotic Systems, a diverse array of assessment methods is employed to thoroughly evaluate the students’ grasp and proficiency in both the theoretical knowledge and practical applications of collaborative robotics.

For Theoretical Lectures:

Quizzes: Frequent quizzes, conducted either in-class or online, will measure students’ understanding of key concepts, theories, and principles discussed in the lectures.
Written Assignments: Individual assignments will require students to apply theoretical knowledge to practical scenarios, encouraging the development of critical thinking and problem-solving abilities.
Midterm and Final Exams: These comprehensive assessments will test students’ overall grasp of the course content, incorporating various types of questions such as multiple-choice, short answer, and essay-based questions.

For Practical Laboratory Work:

Lab Reports: Detailed lab reports will be mandatory, where students document their experimental procedures, outcomes, and analytical insights. Criteria for evaluation will include clarity, methodology, results, and depth of analysis.
Oral Presentations: Presentations of lab work to the class are required, with assessments focusing on presentation skills, clarity of content, and engagement with the audience.

Assessment Criteria:

For Theoretical Lectures:

Knowledge and Understanding: Evaluation of students’ grasp of core concepts, theories, and principles specific to collaborative robotics.
Analytical and Problem-Solving Skills: Assessment of the ability to analyze complex situations, evaluate various solutions, and make informed decisions within the context of collaborative robotics.
Communication Skills: Assessment of effectiveness in articulating ideas, designs, and solutions clearly and engagingly.
Teamwork and Collaboration: Evaluation of the ability to work effectively in teams, contributing to collective goals.
Application of Technology: Measurement of proficiency in employing relevant technologies, tools, and software essential in collaborative robotics.

For Laboratory Work:

Technical Skills: Assessment of the application of technical skills and knowledge to practical collaborative robotics solutions.
Quality of Work: Evaluation of the ability to produce high-standard work that meets established criteria.
Creativity and Innovation: Measurement of the ability to think creatively and apply innovative solutions in collaborative robotics.
Attention to Detail: Assessment of meticulousness in ensuring accuracy and thorough documentation in robotics solutions.
Time Management: Evaluation of the ability to complete lab tasks within allocated timeframes.

Quantitative Performance Indicators:

For Lectures:

Attendance and Participation: Mandatory attendance of at least 80% of lectures and active participation in discussions.
Homework and Quizzes: Completion of all homework and quizzes with at least 60% accuracy.
Midterm Exam: A minimum of 50% score required.

For Laboratory Work:

Lab Participation: Full participation in lab sessions is required.
Lab Reports: Timely submission of all lab reports with a minimum of 60% score.
Lab Assignments: Completion of all assignments with at least 60% accuracy.
Lab Exams: A minimum score of 50% required.

For the Final Exam:

Understanding and Knowledge: At least 70% of lecture-related questions must be answered correctly.
Application and Analysis: Minimum 50% score on case study analysis, problem-solving, and application questions.
Critical Evaluation: A minimum of 50% on essay questions.
Overall Performance: A total exam score of 50% or above is considered passing.

Lectures

Unit 1: Foundations of Collaborative Robots: Concepts and Characteristics (2 hours)

Lecture/Course Objectives and List of References
Introduction to Basic Concepts of Collaborative Robots
Specific Characteristics in the Construction and Use of Collaborative Robots
Overview of the Evolution and Advancements in Collaborative Robotics
Practical Case Studies Highlighting the Unique Features of Collaborative Robots

Unit 2: Mathematical Modelling of Collaborative Robots (2 hours)

Introduction to Mathematical Modelling in Robotics
Efficient Study Methods for Kinematics and Workspace Modelling of Collaborative Robots
The Role and Benefits of the Supplementary Axis (Seventh Axis) in Robotic Design
Case Studies and Examples of Mathematical Modelling in Collaborative Robotics
Hands-on Exercises in Kinematic Analysis and Workspace Modelling

Unit 3: Intelligent Algorithms for Visual Object and Gesture Recognition (2 hours)

Fundamentals of Intelligent Algorithms in Robotics
Techniques for Visual Object Recognition in Collaborative Robots
Gesture Recognition Technologies and Their Applications
Practical Implementation of Visual and Gesture Recognition Algorithms
Interactive Demos and Exercises in Object and Gesture Recognition

Unit 4: Interactive Control Platforms for Collaborative Robots (2 hours)

Overview of Interactive Control Platforms in Robotics
Utilization of Smart/Advanced Tools and Equipment in Collaborative Robotics
Case Studies on Effective Interactive Control Systems
Practical Exercises Using Different Interactive Control Platforms
Evaluation of Advanced Control Technologies in Robotics

Unit 5: AI Algorithms in Collaborative Robots: Roles and Implementation (2 hours)

Introduction to Artificial Intelligence Algorithms in Robotics
Role and Functionality of AI in Collaborative Robots
Step-by-Step Implementation of AI Algorithms in Robotic Applications
Real-World Applications and Case Studies
Hands-on Experience with AI-Driven Collaborative Robots

Unit 6: ROS Operating System in Collaborative Robotics (2 hours)

Introduction to ROS (Robot Operating System) in Robotics
Implementing Different Collaborative Robots Using ROS
Advantages and Challenges of ROS in Collaborative Robotics
Practical Demonstrations of ROS in Action with Collaborative Robots
Exercises and Case Studies Focused on ROS Applications

Unit 7: Non-Conventional Algorithms for Human-Robot Interaction (2 hours)

Exploration of Non-Conventional Algorithms in Robotics
Innovative Approaches to Intelligent Human-Robot Interaction
Use Cases and Applications of Advanced Interaction Algorithms
Hands-on Implementation of Non-Conventional Interaction Techniques
Practical Demonstrations and Interactive Sessions

Each unit is crafted to provide a comprehensive understanding of collaborative robots, starting from basic concepts to advanced applications, ensuring that students gain both theoretical knowledge and practical skills.

Lab Work

Unit 1: ABB-YuMi Collaborative Robot: Operation and Programming Basics (2 hours)

Introduction to the ABB-YuMi collaborative robot
Basics of operation and programming of ABB-YuMi
Running example programs on the ABB-YuMi
Hands-on practice with ABB-YuMi operation and programming

Unit 2: KUKA iiwa LBR: Operation and Programming Fundamentals (2 hours)

Overview of the KUKA iiwa LBR collaborative robot
Essential operation and programming techniques for KUKA iiwa LBR
Executing sample programs on KUKA iiwa LBR
Practical exercises in operating and programming the KUKA iiwa LBR

Unit 3: UR5e Collaborative Robot: Operation and Programming (2 hours)

Introduction to the UR5e collaborative robot
Basic operation and programming skills for UR5e
Running UR5e example applications
Hands-on activities with the UR5e robot

Unit 4: Operating and Programming the MAiRA Collaborative Robot (2 hours)

Essentials of MAiRA collaborative robot operation
Programming basics for the MAiRA robot
Practical demonstrations with the MAiRA robot
Interactive exercises in programming and operating MAiRA

Unit 5: Mathematical Modelling and Workspace Analysis of Robotic Arms (2 hours)

Parametric mathematical modelling of 6 and 7-axis robotic arms
Workspace analysis and trajectory generation techniques
Case studies and examples of mathematical modelling
Hands-on exercises in workspace analysis and trajectory planning

Unit 6: Interactive Application Development with ABB-YuMi (2 hours)

Framework for developing interactive applications with ABB-YuMi
Step-by-step guide to creating an ABB-YuMi interactive application
Practical session on ABB-YuMi application development
Group project on creating a custom interactive application

Unit 7: Interactive Application Development with UR5e (2 hours)

Overview of developing interactive applications using UR5e
Process of programming and implementing UR5e interactive applications
Hands-on project to develop an interactive application with UR5e
Collaborative workshop and discussion

Unit 8: Advanced Interaction with KUKA iiwa LBR: Voice, Visual, and Gesture Recognition (2 hours)

Integrating voice, visual, and gesture recognition with KUKA iiwa LBR
Development process for advanced interactive applications
Practical exercises in advanced interaction techniques
Group activity to implement a multifaceted interactive application

Unit 9: Haptic Feedback Control with KUKA iiwa LBR: Master-Slave Concept (2 hours)

Fundamentals of haptic feedback control
Implementing the master-slave concept on KUKA iiwa LBR
Case study and practical demonstrations
Hands-on practice in developing haptic feedback control systems

Unit 10: AI-Driven Event Detection in Collaborative Robotics Programming (2 hours)

Utilizing AI tools for event detection in collaborative robot programming
Techniques for programming collaborative robots in active environments
Practical exercises with AI tools in robotic programming
Collaborative project on AI integration in robotic event detection

Unit 11: Developing Cognitive Applications with MAiRA (2 hours)

Cognitive applications in collaborative robotics
Step-by-step guide to programming cognitive functions in MAiRA
Interactive session on cognitive application development
Group project on MAiRA-based cognitive applications

Unit 12: Adaptive Control in Dynamic Environments with Collaborative Robots (2 hours)

Techniques for adaptive control in dynamic environments
Case studies on adaptive control with collaborative robots
Practical exercises in dynamic environment adaptation
Collaborative workshop on adaptive control strategies

Unit 13: Interactive Applications with Multiple Collaborative Robots (2 hours)

Framework for developing applications with multiple collaborative robots
Techniques for synchronizing and coordinating multiple robots
Hands-on group activity to develop a multi-robot interactive application
Discussion and analysis of multi-robot system dynamics

Each unit is tailored to provide students with a comprehensive understanding and practical skills in specific aspects of collaborative robots, from basic operation to advanced application development.

Supporting Infrastructure

Hardware Infrastructure

Collaborative Robots: Our lab is equipped with a range of cobots to provide diverse learning experiences:
- ABB-YuMi: A dual-arm cobot known for its precision and safety in close human interaction.
- KUKA iiwa LBR: Stands out for its sensitive touch and flexible task programming.
- UR5e: Renowned for its versatility and ease of integration in various applications.
- MAiRA: Known for its cognitive capabilities and advanced interaction features.
Supplementary Equipment: Each cobot workstation will be equipped with necessary peripherals such as grippers, sensors, and tools for specific tasks and applications.

Software Infrastructure

- Vendor-specific Software: Such as ABB’s RobotStudio, KUKA’s KUKA|prc, and UR’s Polyscope, providing interfaces specific to each robot model.
- MATLAB and SolidWorks: For mathematical modelling and integration of custom robot designs into simulation.

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