Thesis Opportunity- Optimization of Placement Strategies in Robotic Palletizing

What the Master Thesis is about/background to the problem to investigate

Automated palletizing systems must continuously decide where and how objects should be placed on a pallet. The quality of these decisions has a direct impact on pallet fill ratio, throughput, and overall system efficiency.

Many packing and palletizing algorithms have been proposed, ranging from simple heuristics to advanced optimization techniques. While these approaches can perform well under certain conditions, their suitability depends on factors such as the available knowledge about incoming objects, computational requirements, and the constraints of real-world robotic systems.

In practical applications, decisions often need to be made with incomplete information and under changing conditions. Furthermore, solutions that perform well in simulation may not always translate directly to industrial environments where positioning errors, object variations, and other uncertainties are present.

This thesis will investigate optimization of placement strategies for vision-guided robotic palletizing. The work may include studying existing palletizing algorithms, evaluating their strengths and limitations, and exploring how information about current and future objects can be used to improve palletizing performance.

The master thesis work focuses on the following/example of research questions

• What existing packing and palletizing algorithms are suitable for robotic palletizing applications?
• How does access to information about future incoming objects affect palletizing performance?
• What trade-offs exist between pallet fill ratio, computational complexity, and cycle time?
• How do different placement strategies compare in realistic palletizing scenarios?
• How well do results obtained in simulation transfer to real robotic palletizing systems?

Prerequisites

This thesis will involve programming, algorithm development, mathematics, optimization, and computer vision. You should have a strong interest in problem solving and software development. Experience within optimization, computer vision, robotics, or related fields is beneficial.

Contact

For more information about the position, contact:

Fredrik Lindgren, Software Developer, +46 722 26 77 33, fredrik.lindgren@sick.se

Anders Moe, Algorithm Developer, anders.moe@sick.se

We warmly welcome your application — please submit it no later than October 25th.

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As a Machine Vision Innovation Center, SICK Linköping develops advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.

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