Projects

Vision-aided Path planning

Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, We proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot’s onboard vision.

Voltage Control based on ISS Neural Certificates

We Developed methods for stabilizing large scale power systems based on Input-to-State Stability (ISS) Lyapunov-based neural certificate, by treating a large system as an interconnection of smaller subsystems. Each ISS Lyapunov function of subsystem could be collected to prove the global stability of power system.

Multi-robot Social Navigation

We Developed deep reinforcement learning (DRL) based social navigation approach for multiple intelligent robots to safely move in pedestrian-rich environments via cooperative perception.