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About

Menglu Lan is a robotics researcher whose work focuses on autonomous navigation, motion planning, and simulation for unmanned aerial vehicles (UAVs) and mobile robots. Her key contributions lie in developing practical, computationally efficient frameworks for trajectory generation and local motion planning in constrained and GPS-denied environments. Her most-cited paper (42 citations) introduces a model predictive control approach using boundary state constrained motion primitives, enabling fast replanning for mobile robots with limited onboard computation. Lan also developed a high-fidelity quadrotor simulator using ROS and Gazebo (32 citations), providing a safe, cost-effective platform for testing UAV systems before flight trials. Additionally, she proposed a stereo and rotating laser framework for UAV navigation in GPS-denied indoor environments (12 citations), integrating active and passive sensors for robust state estimation and mapping. Her work on axis-coupled trajectory generation using smoothing splines further advances smooth, efficient path planning for chains of integrators. Through these contributions, Lan has established herself as a researcher dedicated to bridging simulation and real-world deployment, making autonomous systems more reliable and accessible.