Brandon Caasenbrood works as a Ph.D. candidate in the Dynamics and Control group within the Mechanical Engineering department. He is currently involved in the Wearable Robotics perspective program, which explores soft robotics -- a subfield of robotics where rigid materials are substituted for softer alternatives. Naturally, these systems have major human-robot safety benefits relevant to wearable robotics. Besides, their intrinsic softness allows for higher-degree mobility that is difficult to achieve in their rigid counterparts. The main inspiration for soft robots stems from biology, with the aim of achieving similar performance and dexterity as biological creatures. Although the field has made significant steps have been towards bridging biology and robotics, its innate infinite dimensionality poses substantial challenges on design, modeling, and control. The diligence of achieving similar precision and speed to nowadays’ rigid robots, and ultimately nature, stresses the paramount importance of novel engineering strategies tailored for soft robotics. His research tackles the problems within the design and control of these soft robots by exploring first‑principle methods. His interest lies in the computational design of continuum structure, nonlinear dynamics, and model-based control in the context of bio‑inspired soft robots. The emphasis here is on bridging the gaps between nature and robotics on a hardware and software level.


Brandon Caasenbrood received his master's degree in Mechanical Engineering from the Eindhoven University of Technology (TU/e) in 2017. In 2016, he did an internship project at the RMIT Melbourne University focussing on nonlinear Kalman filters for drones using onboard inertial and ultra-sound sensors. After his master's, he started as junior researcher at the same university. He is currently pursuing his Ph.d within the field of soft robotics, primarily focused on model-based control and design optimization of soft robotic structures. This research is part of the Wearable Robotics perspective program.

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