Last modified: 25 Oct 2024 12:46
Intelligent Robotics for Energy Infrastructure is designed to provide students with a comprehensive understanding of how robotic technologies can be intelligently applied to enhance the design, construction and maintenance of energy infrastructure. The course explores the intersection of robotics, artificial intelligence and energy systems, aiming to equip students with the skills and knowledge required to address contemporary challenges in the rapidly evolving energy sector.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The course delves into the transformative realm of intelligent robotics as applied to energy infrastructure. Participants will explore state-of-the-art technologies and methodologies, focusing on designing, constructing and maintaining efficient and resilient energy systems. Topics include advanced sensors, autonomous systems, human-robot collaboration and digital twins. The course combines theoretical insights with hands-on applications, preparing students to lead innovations in the integration of intelligent robotics within the energy sector.
Course Content
1. Introduction
2. Sensing for asset integrity management The students will be introduced to the role of sensors and electronics for transducing information about an asset, e.g., temperature, humidity, visual and vibration. The challenges of data management and communications will also be discussed.
3. Robotics and systems engineering Students will be introduced to the challenges and benefits of deploying remotely operated vehicles and sensors, and understand the drivers for autonomous operations and resident robotic systems.
4. Localisation and mapping The students will be presented with an overview of the key techniques for enabling mobile robots to localise themselves, map their environments or do both simultaneously.
5. Machine learning and data analysis The students will be introduced to machine learning as a tool for understanding and interpreting noisy real-world sensor data
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Written feedback will be provided via MyAberdeen. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Appreciate the difficulty, and projected benefits, of deploying remotely operated vehicles and sensors, and understand the drivers for autonomous operations and resident robotic systems |
| Procedural | Evaluate | Understand and evaluate the strengths and limitations of various localization technologies, including feature-based and grid-based approaches, and make informed decisions based on application requir |
| Assessment Type | Summative | Weighting | 70 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Understand the role of sensors and electronics for transducing information about an asset, e.g.: temperature, humidity, visual, and vibration as well as the inherent challenges of data management. |
| Conceptual | Understand | Appreciate the difficulty, and projected benefits, of deploying remotely operated vehicles and sensors, and understand the drivers for autonomous operations and resident robotic systems |
| Procedural | Apply | Appreciate the role of machine learning tools and methods for analysing and interpreting noisy real-world sensor data, such as classifier models for time-series data |
| Procedural | Evaluate | Understand and evaluate the strengths and limitations of various localization technologies, including feature-based and grid-based approaches, and make informed decisions based on application requir |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Understand the role of sensors and electronics for transducing information about an asset, e.g.: temperature, humidity, visual, and vibration as well as the inherent challenges of data management. |
| Conceptual | Understand | Appreciate the difficulty, and projected benefits, of deploying remotely operated vehicles and sensors, and understand the drivers for autonomous operations and resident robotic systems |
| Procedural | Evaluate | Understand and evaluate the strengths and limitations of various localization technologies, including feature-based and grid-based approaches, and make informed decisions based on application requir |
| Procedural | Apply | Appreciate the role of machine learning tools and methods for analysing and interpreting noisy real-world sensor data, such as classifier models for time-series data |
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