Phat C. Vo

• Developed a fleet management system to coordinate AMRs operating on shared indoor/outdoor roads.
• Implemented task scheduling, multi-robot path planning, and real-time conflict resolution strategies.
• Integrated server communication for monitoring, logging, and remote control.

• Led the motion planning and control team in global navigation, local trajectory generation, behavioral planning, and collision risk assessment.
• Built simulation tools to validate the full autonomous system, especially behavior planning.
• Developed real-time communication and data integration between the robot and server.

• Implemented local planning and motion tracking algorithms in the MORAI simulator.
• Integrated locally developed algorithms into the server-based simulation environment via the bridge node.
• Developed a hybrid control framework for concurrent simulation and real robot operation, using simulation outputs to drive the physical robot.

• Developed a simulation environment for autonomous vehicles with customizable scenarios.
• Used the environment to evaluate and demonstrate local planning and decision-making algorithms.

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.