Expérience

Abstract

Hybrid Systems Lab (UCSC, PI: Ricardo Sanfelice) Graduate Student Researcher • MPC-based Tracking Control for Hybrid Systems Mar 2022 Present – Designed a model predictive controller for hybrid systems to track motion plans with proven asymptotic stability property. • Robotics Applications Projects Sep 2021 Present – Implemented a tracking controller for self-driving vehicles with global invariance property. – Implemented a set-based planner for drones considering obstacles exhibiting hybrid dynamics. • RRT Motion Planning Algorithm for Hybrid Systems Sep 2021 Mar 2022 – Designed an RRT-based motion planning algorithm for hybrid systems, called HyRRT, with the proven probabilistic completeness property. Implemented a HyRRT software tool that improves the computation performance by 95.5%. • Feasible Motion Planning for Hybrid Systems Sep 2018 Mar 2021 – Mathematically defined the motion planning problem, systematically formalized the propagation, reversal, and concatenation operations for hybrid systems, and designed a motion planning algorithm template for hybrid systems with proved completeness properties.