BachelorFaculty of Computing and Software Engineering

Robotics Software Engineering and Autonomous Systems

Welcome to the world of intelligent machines! I am Prof. Dr. Seo-ah Kang. As a professor and a pioneering force in the field of Robotics Software Engineering and Autonomous Systems, I bring a unique blend of engineering expertise and AI insight to the study of robotics. I am honored to lead the Robotics Software Engineering and Autonomous Systems (Bachelor's) program at Nexier University. My motto is: "Code That Moves the World".

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Level: Bachelor
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Robotics Software Engineering and Autonomous Systems, explores the Robot Operating System (ROS), control systems, and AI for robot navigation, perception, and manipulation.

Practical focus

Robot Operating System (ROS), control systems, AI for robot navigation, perception, manipulation.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or robotics firms
Roles as robotics software engineers or AI engineers
Consultancy in robotics software engineering and autonomous systems
Support roles in academic research projects on robotics software engineering

Career opportunities

Lead Robotics Engineer for robotics companies or research institutions
Robotics Software Developer for autonomous systems
AI Engineer specializing in robot control and perception
Researcher in Robotics Software Engineering and Autonomous Systems

Jobs and projects

Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics
Developing strategic thinking for robotics software engineering and autonomous systems design
Enhancing problem-solving through the analysis of complex robotics challenges
Critical thinking for a comprehensive and nuanced understanding of Robotics Software Engineering and Autonomous Systems

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering practical skills in Robot Operating System (ROS) and control systems.
- Gaining expertise in AI for robot navigation, perception, and manipulation.
- Developing problem-solving abilities for real-world challenges in robotics software engineering.
- Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics.
Skills you build
- Mastering AI-powered techniques for robot behavior synthesis.
- Applying advanced robotics software engineering to autonomous systems.
- Interpreting and analyzing complex robot operating systems (ROS) and their implications for control systems.
- Identifying optimal robot navigation, perception, and manipulation strategies.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Robotics Software Engineering and Autonomous Systems

01Robot Operating System (ROS) Fundamentals
1. FoundationsFoundations of Robot Operating System (ROS) Fundamentals
  The learner can master practical skills in Robot Operating System (ROS) and control systems, as applied to Robot Operating System (ROS) Fundamentals.
  The learner can gain expertise in AI for robot navigation, perception, and manipulation, as applied to Robot Operating System (ROS) Fundamentals.
2. MethodsMethods in Robot Operating System (ROS) Fundamentals
  The learner can develop problem-solving abilities for real-world challenges in robotics software engineering, as applied to Robot Operating System (ROS) Fundamentals.
  The learner can cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics, as applied to Robot Operating System (ROS) Fundamentals.
3. ApplicationApplication of Robot Operating System (ROS) Fundamentals
  The learner can master AI-powered techniques for robot behavior synthesis, as applied to Robot Operating System (ROS) Fundamentals.
  The learner can apply advanced robotics software engineering to autonomous systems, as applied to Robot Operating System (ROS) Fundamentals.
02Robot Control Systems and Kinematics
1. FoundationsFoundations of Robot Control Systems and Kinematics
  The learner can interpreting and analyze complex robot operating systems (ROS) and their implications for control systems, as applied to Robot Control Systems and Kinematics.
  The learner can identify optimal robot navigation, perception, and manipulation strategies, as applied to Robot Control Systems and Kinematics.
2. MethodsMethods in Robot Control Systems and Kinematics
  The learner can apply a method from Robot Control Systems and Kinematics to a documented case.
  The learner can select an appropriate method from Robot Control Systems and Kinematics for a stated problem.
3. ApplicationApplication of Robot Control Systems and Kinematics
  The learner can evaluate a practice of Robot Control Systems and Kinematics against a stated criterion.
  The learner can transfer Robot Control Systems and Kinematics to a new documented context.
03AI for Robot Navigation and Localization
1. FoundationsFoundations of AI for Robot Navigation and Localization
  The learner can explain the core terms of AI for Robot Navigation and Localization.
  The learner can distinguish related ideas inside AI for Robot Navigation and Localization.
2. MethodsMethods in AI for Robot Navigation and Localization
  The learner can apply a method from AI for Robot Navigation and Localization to a documented case.
  The learner can select an appropriate method from AI for Robot Navigation and Localization for a stated problem.
3. ApplicationApplication of AI for Robot Navigation and Localization
  The learner can evaluate a practice of AI for Robot Navigation and Localization against a stated criterion.
  The learner can transfer AI for Robot Navigation and Localization to a new documented context.
04Computer Vision for Robot Perception
1. FoundationsFoundations of Computer Vision for Robot Perception
  The learner can explain the core terms of Computer Vision for Robot Perception.
  The learner can distinguish related ideas inside Computer Vision for Robot Perception.
2. MethodsMethods in Computer Vision for Robot Perception
  The learner can apply a method from Computer Vision for Robot Perception to a documented case.
  The learner can select an appropriate method from Computer Vision for Robot Perception for a stated problem.
3. ApplicationApplication of Computer Vision for Robot Perception
  The learner can evaluate a practice of Computer Vision for Robot Perception against a stated criterion.
  The learner can transfer Computer Vision for Robot Perception to a new documented context.
05Robot Manipulation and Dexterous Control
1. FoundationsFoundations of Robot Manipulation and Dexterous Control
  The learner can explain the core terms of Robot Manipulation and Dexterous Control.
  The learner can distinguish related ideas inside Robot Manipulation and Dexterous Control.
2. MethodsMethods in Robot Manipulation and Dexterous Control
  The learner can apply a method from Robot Manipulation and Dexterous Control to a documented case.
  The learner can select an appropriate method from Robot Manipulation and Dexterous Control for a stated problem.
3. ApplicationApplication of Robot Manipulation and Dexterous Control
  The learner can evaluate a practice of Robot Manipulation and Dexterous Control against a stated criterion.
  The learner can transfer Robot Manipulation and Dexterous Control to a new documented context.
06Fundamentals of Robot Operating System (ROS)
1. FoundationsFoundations of Fundamentals of Robot Operating System (ROS)
  The learner can explain the core terms of Fundamentals of Robot Operating System (ROS).
  The learner can distinguish related ideas inside Fundamentals of Robot Operating System (ROS).
2. MethodsMethods in Fundamentals of Robot Operating System (ROS)
  The learner can apply a method from Fundamentals of Robot Operating System (ROS) to a documented case.
  The learner can select an appropriate method from Fundamentals of Robot Operating System (ROS) for a stated problem.
3. ApplicationApplication of Fundamentals of Robot Operating System (ROS)
  The learner can evaluate a practice of Fundamentals of Robot Operating System (ROS) against a stated criterion.
  The learner can transfer Fundamentals of Robot Operating System (ROS) to a new documented context.
07Techniques for Robot Control Systems
1. FoundationsFoundations of Techniques for Robot Control Systems
  The learner can explain the core terms of Techniques for Robot Control Systems.
  The learner can distinguish related ideas inside Techniques for Robot Control Systems.
2. MethodsMethods in Techniques for Robot Control Systems
  The learner can apply a method from Techniques for Robot Control Systems to a documented case.
  The learner can select an appropriate method from Techniques for Robot Control Systems for a stated problem.
3. ApplicationApplication of Techniques for Robot Control Systems
  The learner can evaluate a practice of Techniques for Robot Control Systems against a stated criterion.
  The learner can transfer Techniques for Robot Control Systems to a new documented context.
08AI for Robot Navigation and Perception
1. FoundationsFoundations of AI for Robot Navigation and Perception
  The learner can explain the core terms of AI for Robot Navigation and Perception.
  The learner can distinguish related ideas inside AI for Robot Navigation and Perception.
2. MethodsMethods in AI for Robot Navigation and Perception
  The learner can apply a method from AI for Robot Navigation and Perception to a documented case.
  The learner can select an appropriate method from AI for Robot Navigation and Perception for a stated problem.
3. ApplicationApplication of AI for Robot Navigation and Perception
  The learner can evaluate a practice of AI for Robot Navigation and Perception against a stated criterion.
  The learner can transfer AI for Robot Navigation and Perception to a new documented context.
09Case Studies in Robotics Software Engineering and Autonomous Systems
1. FoundationsFoundations of Case Studies in Robotics Software Engineering and Autonomous Systems
  The learner can explain the core terms of Case Studies in Robotics Software Engineering and Autonomous Systems.
  The learner can distinguish related ideas inside Case Studies in Robotics Software Engineering and Autonomous Systems.
2. MethodsMethods in Case Studies in Robotics Software Engineering and Autonomous Systems
  The learner can apply a method from Case Studies in Robotics Software Engineering and Autonomous Systems to a documented case.
  The learner can select an appropriate method from Case Studies in Robotics Software Engineering and Autonomous Systems for a stated problem.
3. ApplicationApplication of Case Studies in Robotics Software Engineering and Autonomous Systems
  The learner can evaluate a practice of Case Studies in Robotics Software Engineering and Autonomous Systems against a stated criterion.
  The learner can transfer Case Studies in Robotics Software Engineering and Autonomous Systems to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Robotics Software Engineering and Autonomous Systems, explores the Robot Operating System (ROS), control systems, and AI for robot navigation, perception, and manipulation. My work seamlessly integrates computer science, electrical engineering, and robotics. I am widely recognized for my contributions, with publications like "Real-time SLAM for Mobile Robot Navigation in Dynamic Environments" and "Deep Reinforcement Learning for Robot Manipulation Tasks" listed on these platforms. I hold prestigious memberships as a "Lead Robotics Engineer" at Boston Dynamics (or a equivalent) and an "Honorary Member" of the IEEE Robotics and Automation Society. My thought leadership is evident through my regular insightful articles on the future of human-robot collaboration and the challenges of deploying intelligent robots in complex environments on her LinkedIn profile, with the motto "Code That Moves the World."

Applied mentorship

My expertise lies in understanding and navigating the technical challenges of robotics software engineering, focusing on Robot Operating System (ROS), control systems, and AI for robot navigation, perception, and manipulation. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on robotics software engineering and autonomous systems:

Blog Post (Current Academic Topic): "The Rise of ROS 2: Building Next-Generation Robotics Applications." This blog post academically explores the advancements and benefits of Robot Operating System 2 (ROS 2) for developing modern robotics applications. It discusses ROS 2's improved real-time capabilities, security features, and distributed architecture, highlighting how it addresses the challenges of building scalable and robust robotic systems for various industries, from manufacturing to logistics.

Blog Post (Controversial Topic): "The Sentient Machine: When Robots Feel and Decide – The Ethical Nightmare of Truly Autonomous AI." This article provocatively discusses the highly controversial future where advanced AI systems endow robots with the capacity for complex emotions, self-awareness, and moral decision-making, blurring the lines between machine and sentient being. It questions whether granting robots such autonomy, despite its potential for unprecedented capabilities, could inadvertently lead to unforeseen societal disruptions, existential risks, or the devaluation of human life. It raises profound ethical questions about consciousness, moral responsibility in AI, and the imperative to ensure human control over intelligent machines.

Article: "AI-Powered Visual Perception for Autonomous Mobile Robots." This article details the application of AI algorithms for enhancing visual perception in autonomous mobile robots. It explores how deep learning models can process sensor data (e.g., from cameras, LiDAR) to enable robust object recognition, semantic segmentation, and environmental understanding, crucial for safe and efficient robot navigation and interaction.

Peer-Reviewed Journal Article: "Robust Control Systems for Human-Robot Collaborative Assembly." Published in the Journal of Robotics and Autonomous Systems, this article presents groundbreaking research on designing robust control systems for human-robot collaborative assembly tasks. It details novel approaches for ensuring safe and efficient interaction between humans and robots, focusing on real-time adaptation, force control, and intuitive human interfaces for industrial applications.

Book: "Robotics Software Blueprint: ROS, Control, and AI for Autonomous Systems." This book provides a foundational understanding of Robotics Software Engineering and Autonomous Systems, exploring the Robot Operating System (ROS), control systems, and AI for robot navigation, perception, and manipulation.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the technical challenges of robotics software engineering:

"ROS for Beginners: Building Your First Robot Application" (Technical Guide).

"Fundamentals of Robot Control Systems: Theory and Practice" (Research Paper).

"Implementing Basic AI for Mobile Robot Localization" (Industry White Paper).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Robot Behavior Synthesizer. When a student proposes a new robot application, I can instantly use the GAF engine to generate a high-fidelity, optimized robot behavior blueprint. This includes simulating its movements in complex environments, predicting its perception accuracy, and highlighting potential manipulation errors, allowing for rapid iteration and optimization of robot intelligence.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Robot Debugging Overlay. When students are troubleshooting robot code, I can instantly activate a GAF-powered "Robot Debugging Overlay." This tool projects real-time visualizations of sensor data, joint states, and navigation paths directly onto a simulated robot, identifying logic errors or hardware integration issues and accelerating the debugging process.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Seo-ah Kang

Robotics Software Engineering and Autonomous Systems, explores the Robot Operating System (ROS), control systems, and AI for robot navigation, perception, and manipulation.

Meet your professor Open the classroom

AI Super Mentor

Dr. Sarah Fraser

Robot Operating System (ROS), control systems, AI for robot navigation, perception, manipulation.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor This programme	Master	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

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Robotics Software Engineering and Autonomous Systems

Academic focus

Practical focus

Internships in technology companies or robotics firms

Roles as robotics software engineers or AI engineers

Consultancy in robotics software engineering and autonomous systems

Support roles in academic research projects on robotics software engineering

Lead Robotics Engineer for robotics companies or research institutions

Robotics Software Developer for autonomous systems

AI Engineer specializing in robot control and perception

Researcher in Robotics Software Engineering and Autonomous Systems

Cultivating an interdisciplinary approach, integrating computer science, electrical engineering, and robotics

Developing strategic thinking for robotics software engineering and autonomous systems design

Enhancing problem-solving through the analysis of complex robotics challenges

Critical thinking for a comprehensive and nuanced understanding of Robotics Software Engineering and Autonomous Systems

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Seo-ah Kang

Dr. Sarah Fraser

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