John P. Swensen grew up in a small farming town in Southern Idaho. He always spent much more time playing on the home computer than working at the family business (often to the chagrin of his father). He receive the BS degree in Electrical Engineering from Utah State University in 2003, the MS and PhD degrees in Mechanical Engineering from The Johns Hopkins University in 2009 and 2012, respectively, working in the Locomotion in Mechanical and Biological Systems (LIMBS) Lab which was a part of the Laboratory for Computational Sensing and Robotics (LCSR). After finishing his PhD, he joined the GRAB Lab at Yale University as a postdoctoral associate (2012-2014) and later transitioned to a position as an Associate Research Scientist (2014-2015). He is currently an Assistant Professor in the School of Mechanical and Materials Engineering at Washington State University, starting in the summer of 2015.
During his undergraduate degree, John began work at the Center for Self Organizing and Intelligent Systems as a sophomore before it spun off as a startup company called Autonomous Solutions, Inc., where he worked for the remainder of his undergraduate degree. He also participated and helped lead a team annually in the USU/Ball Aerospace Robotics Competition, taking first place several times.
After graduating, he went to work as an embedded software engineer at Ball Aerospace and Technologies Corporation working on both the James Webb Space Telescope on the testbed telescope and on various star tracker projects for simulating starfields in real time. While at Ball Aerospace, John realized that nearly all the people who received internal research and development (IR&D) money were those with PhD’s. Ball was in the process of taking voluntary layoffs and John decided it was the perfect time to return to school for a PhD in robotics.
At Johns Hopkins, John’s research was focused on the modeling, estimation, and control of tip steerable needles. In particular, he focused on modeling the torsional dynamics of steerable superelastic Nitinol needles as they were continuously inserted into tissue, with the necessary vision-based feedback control to compensate for the torsion within the needle. He also worked on geometric methods of estimating the rigid body rotation of the tip of the needle, based solely on the ability to measure the heading and having a model of the needle kinematics. The result was an almost-globally convergent observer of attitude.
At Yale University, John worked on simple, scalable modular robotics with adjustable compliance. This is in contrast to many traditional modular robotics where the goal is to create a complex, general-purpose robotic module that is capable of sensing, latching, self-locomotion, and computation. Instead, John’s work focused on making the simple modular robots (called active cells) that could actuate in a single degree of freedom due to an external method of heating and complex behavior came through ensembles of cells.
My current research interest are in high degree-of-freedom compliant systems where through the use of smart materials and construction geometry, the compliance is both tunable and modular. This focus lies at the confluence of my PhD and postdoctoral work. During my PhD work, I realized that many medical devices rely on their high intrinsic compliance to achieve bending and turning necessary to reach a target location, but then this same compliance prohibited the device from exerting large forces at the target location. Thus, combining my postdoctoral work on modules with adjustable compliance and my PhD work for steerable needles, I seek to develop the basic science behind tunable compliant devices. This is not focused on just feedback rendered adjustable compliance, but instead a combination of both feedback control and fundamental changes in device mechanics.
- System Dynamics (ME348): Spring 2017, Fall 2017, Spring 2018
- Mechatronics (ME 401): Fall 2015, Spring 2016, Fall 2018, Spring 2019, Fall 2019
- Robot Kinematics and Dynamics (ME579 Special Topics): Fall 2016. Fall 2018
- Computer Vision (ME579 Special Topics): Fall 2017, Fall 2019
- Fall 2016:
- App development
- Pinewood derby
- Physics and the Pinewood Derby – feature length educational film about the science behind the pinewood derby, intended to get children interested in math and science