Description of the assignment
Vanderlande offers automated warehouse solutions to their customers. Part of these solutions are industrial robot arms that can stack and de-stack products. One type of robot arms can remove products of the same type from a pallet sent by a supplier, called “depalletizing”. Think of a pallet full of stacked boxes of pasta bags that all have to be removed to be distributed further one by one. The other type of robots carefully stacks products of different sizes, weight and stability into roll cages, called “palletizing”. Think of stacking beverages, pasta, chocolate, flour, toilet paper etc. to be delivered to a single supermarket. A planning program gives the robot the ideal placement of all products in the roll cage (heavy products at the bottom, lighter products at the top, place products only on the stable edges of other boxes underneath etc.).
Both types of robots are operating with various sensors and internal models to pick up products and place them on the conveyor belt or in the roll cage. As in any automated system, sensors may not be fully reliable, and models may not capture all aspects of the movement and placement of products. This can lead to products being not placed correctly on the conveyor belt or in the roll cage. In the latter case, incorrect placement of products can lead to a blockage of the robot arm during a next placement, or an instable stack that can in the worst-case collapse during further handling. Vanderlande is collecting extensive data about the movement of the robot arm, the placement and position of products and all desired and undesired events in this palletizing and depalletizing process.
The long-term objective of Vanderlande is to create an improved model of product placement on conveyor belts and in roll cages together with an improved model of the robot arm that can inform the development of better control model for the robot.
This project takes a first step in developing a reliable data-driven model of the process of palletizing products from the available data, that is, how products are actually being placed and stacked in roll cages.
The expected outcomes of this project are:
The Systems Engineering group is part of the Strategy & Markets department, which provides a strategy for our Warehousing Solutions business based on market trends and insights. In line with this strategy, the Systems Engineers drive the development of new concepts & solutions and improvements of existing solutions. Next to this, they support Sales, Operations and Service in correctly deploying our products in customer system designs.
For this internship, the student will be working on ideas to further improve an existing concept, in close cooperation with the responsible Systems Engineers and some experts from the Data Service Development & Data Science team.
Data Service Development & Data Science team within the Global Services organization is responsible for the further digitalization of Life-cycle Services. The team is working on fundamental descriptive and diagnostic solutions and challenging use cases such as models that predict operational behaviour of the logistics systems or failure of physical system components. This is a growing, autonomous team of data scientists, data engineers and data architects that is driving the digitalization of Vanderlande life-cycle services. In our team experiments and entrepreneurial spirit are highly valued
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