Maarten Jongeneel works as a PhD candidate in the Dynamics and Control section within the Faculty of Mechanical Engineering. He is involved in the H2020 European Project on Impact Aware Manipulation (I.AM.) where he is responsible for modeling/validation of impact models for known robots and objects from motion capture and robot proprioception sensor data. These models will serve as the basis to the envisioned impact-aware planning, learning, control, and sensing robot modules, with the focus on three scenarios: tossing, boxing, and grabbing of objects in logistic applications.
ACADEMIC PROFILE
MECHANICAL ENGINEER | PHD STUDENT
RESEARCH PROFILE
ACADEMIC BACKGROUND
Maarten Jongeneel obtained his master's degree in Mechanical Engineering from the Eindhoven University of Technology (TU/e) in 2020. In 2018, he did an internship project at Instituto Superior Técnico in Lisbon focussing on object tracking algorithms. In 2020, he started as a doctoral candidate within the Dynamics and Control section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). His current research interests are nonsmooth mechanics, impact dynamics, visual object tracking, and multibody dynamics.
IMPACT-AWARE MANIPULATION
Europe is leading the market of torque-controlled robots. These robots can withstand physical interaction with the environment, including impacts, while providing accurate sensing and actuation capabilities. I.AM. leverages this technology and strengthens European leadership by endowing robots to exploit intentional impacts for manipulation. I.AM. focuses on impact aware manipulation in logistics, a new area of application for robotics which will grow exponentially in the coming years, due to socio-economical drivers such as booming of e-commerce and scarcity of labor. Withing this project, I am responsible for modeling/validation of impact models for known robots and objects from motion capture and robot proprioception sensor data. These models will serve as the basis to the envisioned impact-aware planning, learning, control, and sensing robot modules, with the focus on three scenarios: tossing, boxing, and grabbing of objects.
PUBLICATIONS
MSc THESIS