Graduation: Knowledge Graphs for Improving Robot Operations in Logistics

Graduation: Knowledge Graphs for Improving Robot Operations in Logistics

Job area Engineering Location Veghel, Netherlands. Contract type Internship

Assignment type: Graduation
Assignment duration: 
ASAP, for six months
Location
Veghel and home office
Educational: 
WO
Desired study: Data Science, Computer Science or related studies
Language: English

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 first objective is to develop a knowledge-graph describing the physical positioning of products on the stack in the roll cage. The graph shall describe products, their relative and absolute position, augmented with sensor information about (in-)correct placement, and event information about robot movement and when and how products are being placed. Altogether, this graph shall describe the process of building a stack in time and space in a single data model.
  • The second objective is to explore reasoning on this knowledge graph through querying and graph mining: How does the stack built from sensor and event information compare to the ideal stack given by the planning program? By comparing different stacks built for different product combinations, are there typical deviations or mistakes in stacking that cause undesired events in the stacking? Can the causes be detected as the stack is being built before undesired events occur?
  • The final objective is to outline data requirements to make the same knowledge-graph based approach also applicable to the de-palletizing step (as currently less data is available for this step).

The expected outcomes of this project are:

  • A proof-of-concept data model for a knowledge graph of the process of building a product stack by a robot arm that addresses the above properties;
  • A proof-of-concept implementation of a data integration and processing pipeline to construct such knowledge graphs from available data sources within Vanderlande;
  • An evaluation of which properties of the process of building a product stack can be answered reliably on the knowledge graph using process mining and graph mining, for example which (combinations of) product properties lead to undesired events in the palletizing process.

Department description
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

Your responsibilities

  • A proof-of-concept data model for a knowledge graph of the process of building a product stack by a robot arm that addresses the above properties;
  • A proof-of-concept implementation of a data integration and processing pipeline to construct such knowledge graphs from available data sources within Vanderlande;
  • An evaluation of which properties of the process of building a product stack can be answered reliably on the knowledge graph using process mining and graph mining, for example which (combinations of) product properties lead to undesired events in the palletizing process.

Minimum requirement

  • Pro-active and independent.
  • Affinity with data and able to introduce new approaches / new data sources.
  • Experience with process mining techniques and/or building knowledge graphs.

Contact
Do you recognize yourself in this challenging profile? Are you looking for an internship in an organization that has been elected as “Best Employer” for years in a row? Please fill out the application form and upload your resume and cover letter. For more information, contact us by e-mail: internship@vanderlande.com or contact Jasper Pijnenburg (Campus Recruiter) by phone: +31 (0)413 – 49 44 08.

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