AI & Robotics for Energy Integrity
Stay ahead in the energy industry with our AI & Robotics for Energy Integrity course. Learn how AI and robotics are transforming inspection through machine learning, deep learning, and photogrammetry. Taught by experts from industry and the University of Houston, this course blends fundamentals with hands-on practice to help you integrate smart inspection tools into your workflow. Ideal for professionals ready to boost efficiency and innovation in real-world energy applications.
Overview
Duration
2-3 hours a week for 7 weeks
Format
Hybrid
Price
Discount may apply
Course Price: $1400
Deadline
Check back Fall 2025 for schedule updates.
Why this Credential?
Integrating robotics into inspection processes significantly enhances efficiency in data collection. The ability of robots to gather extensive data strengthens the demand for robust data analysis and management solutions. Artificial Intelligence (AI) has emerged as a powerful tool for analyzing complex data sets, significantly enhancing efficiency, innovation, and decision-making across various industries. The synergistic use of robots and AI can revolutionize inspection methodologies, streamline processes, and set new industry benchmarks. To stay with the trend, we offer this AI & Robotics for Energy Integrity course that uses popular inspection methods and use cases to provide an ideal introduction of AI for professionals in the energy industry. This course is instructed by subject matter experts from both industry and the University of Houston, and will help participants enhance their understanding of AI and seamlessly integrate AI and robotic solutions into their workflows.
In this course, participants will learn and experience the three popular use-cases and methods that have been widely used in robotic inspection in the energy industry: machine learning and its usage in non-destructive testing (NDT), deep learning that gives robots a 鈥渧ision鈥� to analyze inspection data, and photogrammetry that enable rapid 3D reconstruction essential for digital twining assets in inspection and management. The course offers a detailed summary of fundamental principles, hands-on practice, and an integration showcase. Participants will gain insight into the powerful combination of robots and AI technologies and will also be equipped with practical knowledge on how to implement these technologies effectively in real-world scenarios.
Who Should Enroll?
- Engineers and technicians seeking to boost their robotic inspection and maintenance expertise with AI technology.
- Individuals who are interested in the latest trends in AI and are looking to implement this with robotics for enhanced inspection and maintenance tasks.
- Industry professionals aiming to exploit robotic and AI-based inspection solutions, seeking in-depth knowledge on machine learning, computer vision inspection, and photogrammetry.
Schedule
Check back Fall 2025 for schedule updates.
What You Will Earn
- A digital badge will be issued after successfully completing this course.
- Texas professional development hours and continuing education units available
After successfully completing this module, participants should be able to:
- Gain a comprehensive knowledge of AI鈥檚 role in analyzing and interpreting data derived from robots and become familiar with the model training process.
- Have a solid grounding in deep learning and computer vision, understanding how robots perceive, analyze, and act in their surroundings.
- Apply the skills to use AI in complex data analysis and management while understanding the associated challenges and risks.
- Continue to develop expertise in applying AI and robotics to address the needs, challenges, and opportunities within the energy sector.
Instructors
Dr. Gangbing Song
Moores Professor of Mechanical Engineering
葫芦影业
Dr. Vedhus Hoskere
Assistant Professor of Civil and Environmental Engineering
葫芦影业
Kimberley Hayes
CTO
Valkim Technologies
Matt Alberts
Head of Project Management
Future Technologies Venture
Founder of Societa AI
Dr. Suchet Bargoti
CTO
Abyss Solutions
Pete Peterson
Head of Product Management and Marketing
XaaS Lab