Phat C. Vo

I worked with the Educational Robot Platform (ERP) developed by WeGo Robotics, which was integrated into the MORAI simulation environment for autonomous driving research and development. Using this platform, I implemented and tested core motion planning components, specifically:

• Local planning algorithms, enabling the robot to navigate dynamic environments with smooth and collision-free trajectories.
• Motion tracking and control logic, ensuring accurate path following and stability under varying conditions. To support development and evaluation, I published key planning and tracking data to ROS topics, allowing for real-time visualization, logging, and further analysis. This setup provided a practical and extensible environment for experimenting with and validating autonomous driving algorithms on a compact, education-oriented platform.

Human-Robot Teleoperation (HRT) is the focus of my Ph.D. thesis project, developed using MATLAB and supported by a series of published research works. It aims to establish a reliable and responsive teleoperation framework that balances both human control and robotic autonomy. The project addresses four key aspects:

• Robustness: Stable performance despite uncertainties from the operator, environment, or sensors.
• Performance: Effective task execution with speed and accuracy.
• Perception: Enhancing the operator’s awareness of the remote environment.
• Transparency: Accurately reflecting the environment and robot state to the operator. To find further details in my thesis defense presentation available in the Education section.

This project designs and controls a Variable Stiffness Actuator (VSA) for quadruped robot legs to enhance adaptability, shock absorption, and energy efficiency. The VSA consists of two actuators: one controls joint position, the other adjusts stiffness by modulating an elastic element. This allows real-time tuning of joint compliance independently from position. The control system includes:

• A high-level controller that plans motion and desired forces,
• A low-level controller that adjusts both position and stiffness using sensor feedback. By integrating VSA into each leg, the robot can achieve softer landings, better stability on uneven terrain, and improved safety when interacting with humans. This approach mimics animal-like locomotion and is ideal for dynamic and unpredictable environments.