Master Thesis: Optimizing the Excavation Process of Wheel Loaders Using Reinforcement Learning

Job description

Optimizing the Excavation Process of Wheel Loaders Using Reinforcement Learning: A Focused Approach on Algorithm Selection and Model Development

What you will do

 

This thesis project will delve into the fascinating intersection of reinforcement learning and heavy machinery operation. The main objective is to develop a virtual driver model that optimizes the excavation process of both electric conventional and autonomous wheel loaders in a simulated environment, allowing for a comparative performance analysis. You will have the opportunity to choose the most effective RL algorithm, construct and train the model, and fine-tune it to improve critical performance metrics, including cycle time, energy efficiency, and material handling efficiency.

Who are you?

We are looking for a motivated student with a strong background in computer science, machine learning, robotics, or a related field. Experience with reinforcement learning, simulation environments (e.g., MATLAB, Simulink), and programming skills in Python are highly desirable.

What’s in it for you?

Are you passionate about machine learning, artificial intelligence, and autonomous systems? Do you want to contribute to cutting-edge research that could revolutionize the construction and mining industries? We invite you to join our exciting master’s thesis project aimed at optimizing the excavation process of Wheel Loaders (WLOs) using Reinforcement Learning (RL).

If you are ready to take on this challenge and help shape the future of autonomous machinery, apply for this master’s thesis project today. For more information or to express your interest, please contact: Tutors:

 

Shehr Bano Fatima

shehr.bano.fatima@volvo.com

 

Hiring Manager:

Richard Westerberg

richard.westerberg@volvo.com

 

Join us in this exciting journey to revolutionize the excavation process of wheel loaders using cutting-edge reinforcement learning techniques!

Please note  that this opportunity is suitable for two candidates. You can apply either individually or as a team of two. Last application date is 30th of November 2024.

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Job Category:  Technology Engineering

Organization:  Volvo Construction Equipment

Travel Required:  No Travel Required

Requisition ID:  14283