First Place, Drone Mine Inspection Challenge (Real-World) 2025. "IEEE RAS Summer School on Multi-Robot Systems, Czech Republic"
Second Place, Drone Mine Inspection Challenge (Virtual-World) 2025. "IEEE RAS Summer School on Multi-Robot Systems, Czech Republic"
Minnesota State University Mankato Research Grant ($15,234) from 2024 - Present. "Minnesota State University Mankato"
Third place award - Graduate Student Paper Competition 2023. "Industrial Engineering and Operations Management Society International"
Outstanding Graduate Award 2017. "Nanjing University of Aeronautics and Astronautics, China"
Honor of Excellent Performance 2015. "Nanjing University of Aeronautics and Astronautics, China"
Best Academic Progress Award 2015. "Nanjing University of Aeronautics and Astronautics, China"
International Student Scholarship. Every year from 2013 – 2017. "Nanjing University of Aeronautics and Astronautics, China"
K.D. Nguyen, P. Acharya, T. Burgers “Measurement of Droplet size and Velocities”, Invention. The patent application filed on November 11, 2022, assigned US application serial number 18/054,830.
A Thrilling Win: First Place in the Real-World Challenge and second place in the Virtual Challenge! I was a part of a competition with around 250 participants most of whom are working on the cutting edge robotics research, making up a total of 43 teams from around the world. Our specific challenge was to code two drones to autonomously inspect a simulated mine. The code implementation had to be robust enough to prevent collisions between the drones while each navigated its own path. Our performance was evaluated on four key metrics:
1. No drone collisions
2. All inspection points visited
3. No dynamic constraints violated
4. Completing the mission in the minimum amount of time
In the experimental setup, a robotic manipulator with a horizontal tray moves a rectangular plywood object, and the critical angle and acceleration are calculated and tested. The paper also explores S-curve motion profiles for trajectory planning, ensuring smooth and controlled movement. Using this, a feedback controller is implemented to control the manipulator’s trajectory. Stability is defined based on a critical angle, and the maximum allowable acceleration before tipping occurs is determined. Experimental results confirm the predicted critical acceleration, validating the model's effectiveness. This work is applicable to tasks where robots need to manipulate objects without grasping them, such as in food serving or delicate item handling, offering insights into the dynamics of object stability in robotic manipulation systems.