Bin Lan

About

Bin Lan is a robotics researcher focused on advancing autonomous navigation in complex, unstructured environments—from underground mines to uneven outdoor terrains. His work addresses critical challenges in path planning, obstacle avoidance, and safety-critical control for mobile and rescue robots. Lan’s most cited paper (10 citations) introduces an improved hybrid ant colony optimization and genetic algorithm for multi-map path planning of rescuing robots in mine disaster scenarios, tackling high uncertainty and complex obstacles. He also developed PUTN, a plane-fitting based uneven terrain navigation framework (3 citations), enabling ground robots to traverse rough 3D environments. Additionally, his dynamic control barrier function-based model predictive control method (2 citations) provides real-time, safe obstacle avoidance for mobile robots using LiDAR and DBSCAN clustering. By combining optimization algorithms, geometric modeling, and control theory, Lan’s work pushes the boundaries of robot autonomy in hazardous and unstructured settings, with clear applications in search-and-rescue and field robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   An Improved Hybrid Ant Colony Optimization and Genetic Algorithm for Multi-Map Path Planning of Rescuing Robots in Mine Disaster Scenario

   10 citations · 2025
2. 2
   PUTN: A Plane-fitting based Uneven Terrain Navigation Framework

   3 citations · 2022
3. 3
   Dynamic Control Barrier Function-based Model Predictive Control to Safety-Critical Obstacle-Avoidance of Mobile Robot

   2 citations · 2022

Key Collaborators

Contact & Links