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Master Thesis – Optimize bucket loading for wheel loaders

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Volvo Construction Equipment, a business area within the Volvo Group, is one of the world’s leading manufacturers of construction equipment such as wheel loaders, excavators, articulated haulers, road development equipment and compact equipment. Center for development and manufacturing of Volvo Articulated and Autonomous Haulers is located in Braås, Sweden. We are committed to building a better tomorrow and are proud of our innovative spirit resulting in world leading products, autonomous haulers and the first vehicle built with fossil free steel and much more, always with a great team spirit and collaboration.
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Description

The purpose of the thesis work is to find the optimal solution for bucket loading using reinforcement learning (RL) while loading/unloading a wheel loader (WL).
Reinforcement learning has become a tool for understanding the optimal solution of complex problems. It started from the game theory and is today widely used in the industry to understand the optimal solution based on so called cost functions. An environment has already been created using Python and it is based on the reward policy. In the next step, which calls for this thesis work, a study needs to be carried out by improving the reward policy so that the existing model behaves closer to reality and an optimal solution can be found. The thesis will be performed using the Python language. Further, one can also get familiar with the bucket loading of the WL when handling different materials.

Suitable Background: Mechanical Engineering or similar, applied mathematics, experience in Python.
Thesis Level: Master
Language: Documentation in English
Starting Date: Spring 2024
Number of Students: 1-2

For further information, please contact:

Manoranjan Kumar, manoranjan.kumar@volvo.com
Jonas Johnsson, jonas.johnsson@volvo.com

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