Master Thesis: Collaborative Robotic Arm and Humanoid Interaction for Kitting Tasks in Simulated Factory Environment
Keywords
Robotics, Simulation, Computer vision, Machine Learning, ROS2, Moveit2
Targeted Programs
Computer Engineering/Science, Automation & Mechatronics Engineering
Purpose of the Study
Infotiv, as part of its ongoing research into intelligent automation and simulation, is exploring collaborative robotics in industrial settings. This thesis focuses on the development and training of robotic systems to perform kitting tasks, the process of collecting and organizing components, while ensuring safe and efficient interaction with human workers in a shared workspace.
The study will utilize Gazebo simulator and MoveIt2/ROS2 to simulate and control both a robotic arm and a humanoid robot (as a model of a worker). The goal is to train models that enable the robotic arm to perform kitting tasks in the presence of human workers without any safety cage, ensuring collision avoidance, task coordination, and human safety.
Potential Research Questions
How can machine learning models be trained to perform collaborative kitting tasks in simulation?
What strategies ensure safe human-robot interaction in shared environments?
How can ROS2 and MoveIt2 be leveraged for real-time control and coordination?
What are the best practices for simulating safety-critical scenarios in Gazebo?
Please read more about this project in these links:
https://github.com/infotiv-research/SIMLAN/
https://infotiv-research.github.io/
Who we are looking for
We are looking for 2 master students with a background in mechatronics, computer science/engineering or equivalent program, who wish to conduct their thesis during the spring of 2026. Applicants shall be interested in software development and machine learning and have experience of using Python, ROS2, git, Linux and docker.
Infotiv
TechDev is a department at Infotiv who focuses on SW & HW development and test solutions. We currently consist of 70 technical consultants with diverse backgrounds and experience from many technological fields. Our employees use their expertise to provide tailored solutions to all kinds of challenges, ranging from SW development, machine learning and simulations to project & test management and way of working. One of our key strengths is the friendly atmosphere in our technical community, which provides access to TechDev's collective knowledge through internal collaboration tools and competence leader program, continuously providing updates in the latest tech.
How to Apply
Apply for this thesis no later than 2025-12-31. Assure to attach your resumé and a short summary of why you want to partake in this thesis. Interviews will be held continuously.
For further information, contact: Maria Kindmark Alemyr [maria.alemyr@infotiv.se] +46(0)-76 890 78 72.
