The force between a bicycle tire and the road is difficult to measure and predict. If we were able to accurately estimate these forces in real-time from simple inexpensive sensors, such as inertial measurement units, we could use these force estimates to drastically improve traction control systems for robotically augmented bicycles, scooters, and motorcycles. The challenge of this project is to develop a computational method that accurately estimates the tire forces (magnitude and direction) at each wheels' contact patch using a minimal set of inexpensive and unobtrusive sensors. A solution is likely to involve an inverse dynamics model of a bicycle, signal processing, filtering and estimation, experimental validation, and/or (physics informed) machine learning.
How To Apply
If you would like to apply for this project, please send an approximately half page letter explaining your motivations and interest in the lab and project, CV or resume, a list of courses you've taken, the name of your MSc track, and any other relevant information to firstname.lastname@example.org.