About Me
I am a PhD student @ General Robotics Lab - Duke (MEMS), advised by Dr. Boyuan Chen, where I am currently working on field robotics with multi-agent systems, aiming to enable robust collective intelligence in uncertain environments.
I hold an MPhil & a BEng (1st Hons) from Hong Kong PolyU (AAE). My research/studies were fully funded via Presidential Research Fellowship & APEC Scholarship. I also did an academic exchange at UQ Australia (AE).
Previously, I was a member @ AIRO Lab mentored by Prof. Chih-Yung Wen & Dr. Boyang Li. I also worked as a RA @ HKCRC, a research institute led by Prof. Zexiang Li.
Up'til now, my focus has been on UAVs, UGVs, UUVs, & multi-agents (homo/heterogeneous), with experiences in v-SLAM, traj-opt, MPC, Lyapunov-based control & dynamics. Feel free to ping me for discussions if any of the above interests you; my RTT is usually low :)
Project Highlights
Experimental Non-Robocentric Dynamic Landing of Quadrotor UAVs with On-Ground Sensor Suite [Code] [PDF]
Conducted research on an autonomous landing system for UAVs without onboard vision sensors. Algorithms and methods utilized include SE(3) relative estimation, the iterated extended Kalman filter, quadrotor dynamic modeling, minimum-jerk trajectory optimization, Bézier curves, and a non-inertial PID-feedforward outer-loop controller.
A Bootcamp for Legged Robotics via Biped and Quadruped Simulation [Code]
Self-implemented biped simulator with feedback linearization and a gait controller for a quadruped using Gazebo ROS. Algorithms and methods employed include Euler-Lagrange modeling, the RK Dormand–Prince method, fixed-point searching on Poincaré maps, forward and inverse kinematics, trajectory optimization with polynomials, feedback linearization, quadratic programming, and cycloid trajectories.
Fixed-Time Adaptive Consensus Control for Multi-Quadrotor Subject to External Disturbances Via Deep Reinforcement Learning [PDF]
Contributed to a research project on adaptive control for formation control of UAV swarms. Algorithms and methods employed include sliding-mode control on graph topology, nonlinear disturbance observers, reinforcement learning (RL) for hyperparameter tuning, quadrotor dynamic modeling, Lyapunov stability analysis, and physical experiments with UAV swarms involving communication.
An Adaptive Model Predictive Control for Unmanned Underwater Vehicles subject to External Disturbances and Measurement Noise [Code] [PDF]
Developed an adaptive model predictive controller (MPC) for an unmanned underwater vehicle (UUV) using an error-state extended state observer. Algorithms and methods employed include lumped disturbance observation, error-state space tracking and estimation, Lyapunov stability analysis, and nonlinear MPC.
Toy Convex Solver [Code] [PDF]
Self-implemented a convex solver for equality-constrained minimum-snap trajectory optimization. Algorithms and methods employed include the infeasible start Newton's method and convergence analysis.
Autonomous Dynamic Object Tracking/Following UAV [Code] [PDF]
My honours project during my Bachelor's studies, where I designed a UAV capable of tracking and following dynamic objects. Algorithms and methods employed include DNN (YOLO) implementation and training, linear Kalman filter, and visual servoing with PID.
An Autonomous UGV with LOAM [Code-SITL] [Code-Firmware]
This implementation showcases an autonomous UGV that employs an open-source LOAM algorithm and a custom-developed nonholonomic car controller utilizing PID-feedforward control. The system was demonstrated on the AgileX Mini platform. Additionally, the corresponding Gazebo SITL simulation environment, including the controller, is maintained and open-sourced.
Bug Planner Implementation for Nonholonomic Planner Robots [Code]
This is an implementation of a nonholonomic path planner for robots during my undergraduate studies. Algorithms and methods employed include 2D lidar scan data processing, 2D wheel robot dynamic modeling, the Bug algorithm for path planning, and 2D wheel robot PID control.
Selected Publication
- L.-Y. Lo, B. Li, C.-Y. Wen, and C.-W. Chang, “Experimental Non-Robocentric Dynamic Landing of Quadrotor UAVs with On-Ground Sensor Suite,” IEEE Transactions on Instrumentation and Measurement (TIM), vol. 73, 2024. [link]
- L.-Y. Lo, Y. Hu, B. Li, C.-Y. Wen, and Y. Yang, “An adaptive model predictive control for unmanned under-water vehicles subject to external disturbances and measurement noise,” in 2024 14th Asian Control Conference (ASCC). IEEE, 2024, pp. 01–07. [link]
- L.-Y. Lo, B. Li, C.-Y. Wen, and C.-W. Chang, “Landing a quadrotor on a ground vehicle without exteroceptive airborne sensors: A non-robocentric framework and implementation,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2023, pp. 6080–6087. [link]
- L.-Y. Lo, C. H. Yiu, Y. Tang, A.-S. Yang, B. Li, and C.-Y. Wen, “Dynamic object tracking on autonomous uav system for surveillance applications,” Sensors, vol. 21, no. 23, p. 7888, 2021. Editor’s Choice Article. [link]
- Y. Yang, K. Liu, L.-Y. Lo, T. Huang, Y. Fu, and C.-Y. Wen, “Fixed-time adaptive consensus control for multi-quadrotor subject to external disturbances via deep reinforcement learning,” Aerospace Science and Technology (AST), p. 111133, 2025. [link]
- W. Yang, Z. Tan, L.-Y. Lo, K. Liu, and C.-Y. Wen. “Hierarchical 3D scene graph based metric semantic slam for object mapping and counting,” IEEE Internet of Things Journal, 2025. [link]