Phat C. Vo

This project is designed based on Conflict-Aware Dispatcher for deployment in large factory environments where AMRs operate on shared internal roads (indoor + outdoor). The system enables coordinated operation among robots and seamless interaction with web-based tools and human operators. It serves as the central control layer for scalable, safe, and intelligent fleet management—laying the foundation for smart factory automation. I was responsible for several core features, including:

• Task Scheduling and Dispatching: Dynamically assigns schedules and inspection tasks to each robot based on real-time conditions and system priorities.
• Multi-Robot Path Planning and Conflict Resolution: Manages priority-based rerouting and waiting strategies to prevent congestion and resolve conflicts in real time, especially in narrow roads and intersections shared by multiple robots.
• Monitoring and Visualization: Collaborated with the front-end team to integrate a web-based dashboard that supports real-time, bidirectional messaging and logging database. It provides live updates on robot statuses, and task progress, enabling operators to remotely supervise the entire fleet.

These robots, equipped with steering-based locomotion mechanisms, have been deployed for security patrol tasks in public areas and construction sites, as well as for logistics operations in industrial environments. They autonomously navigate complex, shared internal roadways populated by humans and other vehicles such as trucks and forklifts. I led the Motion Planning team in the concurrent development of autonomous capabilities across multiple robotic platforms. For each project, I developed core autonomy software, including:

• Global Navigation, tailored to scenario requirements and factory map data.
• Local Trajectory Generation Algorithms to ensure smooth and feasible motion control.
• Behavioral Planning for real-time decision-making, including obstacle avoidance and speed profile planning.
• Collision Risk Assessment and Safety Validation to guarantee reliable and safe navigation. To support development and operations, I built a simulation environment and developed a custom user interface to facilitate system interaction and operator control. Additionally, communication with a central server is maintained to enable real-time coordination, remote control, and system status monitoring—fully integrated into the FMS project.

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.