Determining and Reducing the Likelihood of Falling
The state of the art in diagnosing unsafe behavior relies on a combination of human expertise or exhaustive testing. For example, many existing tools to estimate the likelihood that a locomoting system (human or robotic) will fall rely on heuristic measures extracted from physician experience or on perturbing a steady state gait at arbitrary instances in time. As a result, automated tools for predicting falls have not emerged.
Given a model, we have developed optimization tools to explicitly measure the amount of disturbance that a specific gait can tolerate at arbitrary instances during a steady state locomotor pattern. Current work, is focused on improving the scalability of this approach and validating its utility via experimentation.
- R. Vasudevan, “Numerical Advection of Probability Densities for High Dimensional Systems,” in SIAM Conference on Applications of Dynamical Systems, 2015.
- A. Majumdar, R. Vasudevan, M. M. Tobenkin and R. Tedrake, “Convex Optimization of Nonlinear Feedback Controllers via Occupation Measures,” International Journal of Robotics Research, vol. 33, pp. 1209-1230, August 2014. [url]
- V. Shia, R. Vasudevan, R. Bajcsy and R. Tedrake, “Convex Computation of the Reachable Set for Controlled Polynomial Hybrid Systems,” in IEEE Conference on Decision and Control, 2014. [pdf]