A 3d vision cone based method for collision free navigation of a quadcopter UAV among moving obstacles

Zhenxing Ming, Hailong Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiq-uitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.

Original languageEnglish
Article number134
Issue number4
Publication statusPublished - 12 Nov 2021


  • 3D Navigation
  • 3D vision cone
  • Aerial drones
  • Autonomous navigation
  • Collision avoidance
  • Moving obstacles
  • Navigation in dynamic unknown environments
  • Obstacle avoidance
  • Sliding mode control
  • UAVs

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Aerospace Engineering
  • Computer Science Applications
  • Artificial Intelligence

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