Wouter J. Meijer

Delft, South-Holland

Currently, I work as a robotics software engineer at TNO, the Dutch research institute. At TNO, my research focus is combining mapping and task planning to create actionable enviromental representations for robots. Additionally, I specialize in scaling robotic systems and teams from the experimental phase into operational concepts for our customers.

I hold a master’s degree in Cognitive Robotics and a bachelor’s degree in Mechanical Engineering from Delft University of Technology. In my Masters I got hands-on experience with the learning, reasoning, computer vision, mapping and motion planning aspects of mobile robots. During my Bachelor I worked for Skelex and I am proud to have represented the passive exoskeleton technology we were working on at many industrial tech events. I really enjoyed investigating the use cases of potential customers. Often times the exoskeletons would be used for physically demanding jobs for which ‘the robot’ was not good enough yet.

news

Jun 12, 2024	We got 2 papers accepted at RSS, right here in Delft!
Mar 12, 2024	We got papers accepted at ICRA and ICPRAI, I will get the opportunity to present them in Japan and Korea respectively!
Apr 05, 2023	We got our paper accepted at ICRA, so I’m going to London!
Jul 01, 2021	I will start a new challenge as a Robotics Researcher at TNO within the Intelligent Autonomous Systems group!

selected publications

ICRA
Situational Graphs for Robotic First Responders: an application to dismantling drug labs

WJ Meijer, AC Kemmeren, JM Bruggen, and 3 more authors

In ICRA’24 Workshop on Field Robotics, 2024

Abs Bib PDF

In this work, we support experts in the safety domain with safer dismantling of drug labs, by deploying robots for the initial inspection. Being able to act on the discovered environment is key to enabling this (semi-)autonomous inspection, e.g. to open doors or take a closer at suspicious items. Our approach addresses this with a novel environmental representation, the Behavior-Oriented Situational Graph, where we extend on the classical situational graph by merging a perception-driven backbone with prior actionable knowledge via a situational affordance schema. Linking situations to robot behaviors facilitates both autonomous mission planning and situational understanding of the operator. Planning over the graph is easier and faster, since it directly incorporates actionable information, which is critical for online mission systems. Moreover, the representation allows the human operator to seamlessly transition between different levels of autonomy of the robot, from remote control to behavior execution to full autonomous exploration. We test the effectiveness of our approach in a real-world drug lab scenario at a Dutch police training facility using a mobile Spot robot and use the results to iterate on the system design.
@inproceedings{meijer2024situational, title = {Situational Graphs for Robotic First Responders: an application to dismantling drug labs}, author = {Meijer, WJ and Kemmeren, AC and van Bruggen, JM and Haije, T and Fransman, JE and van Mil, JD}, booktitle = {ICRA'24 Workshop on Field Robotics}, year = {2024} }