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Area of Science:

  • Geotechnical Engineering
  • Robotics
  • Remote Sensing

Background:

  • Landslides present significant risks to public safety and infrastructure.
  • Manual slope inspections are challenging due to difficult terrain and vegetation cover.
  • Existing Unmanned Aerial Vehicle (UAV) systems struggle with navigation in dense vegetation.

Purpose of the Study:

  • To develop a comprehensive UAV framework for effective slope inspection in challenging environments.
  • To enhance autonomous navigation and obstacle avoidance capabilities for UAVs operating in dense vegetation.
  • To validate the practical applicability of a LiDAR-based quadrotor for slope inspection tasks.

Main Methods:

  • Development of a LiDAR-based quadrotor system with integrated sensors (LiDAR and cameras).
  • Implementation of a comprehensive software system for assisted obstacle avoidance and autonomous navigation.
  • Conducting field experiments in collaboration with the Hong Kong Civil Engineering and Development Department.

Main Results:

  • The quadrotor demonstrated successful autonomous avoidance of small and dynamic obstacles.
  • The system proved capable of maneuvering effectively within dense vegetation environments.
  • Experimental results validated the potential for practical application in real-world slope inspections.

Conclusions:

  • The developed LiDAR-based quadrotor system offers a robust solution for overcoming limitations of current UAVs in dense vegetation.
  • This technology significantly improves the feasibility and efficiency of slope inspections, contributing to landslide risk management.
  • The system shows practical potential for enhancing infrastructure safety and inspection protocols.