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Complex Environment Path Planning for Unmanned Aerial Vehicles.

Jing Zhang1, Jiwu Li1, Hongwei Yang1

  • 1College of Computer Science and Technology, Chang Chun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary

This study introduces new algorithms for unmanned aerial vehicle navigation in complex urban areas. The branch-selected rapidly-exploring random tree (BS-RRT) and improved GM(1,1) model enhance path planning and obstacle avoidance for safer flights.

Keywords:
narrow passagespath planningpruningtrajectory predictionunmanned aerial vehicles

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Aerospace Engineering

Background:

  • Unmanned aerial vehicles (UAVs) face significant challenges in navigating complex urban environments due to narrow passages and dynamic obstacles.
  • Existing path planning algorithms struggle with the intricacies of urban airspace, impacting flight safety and efficiency.

Purpose of the Study:

  • To develop and evaluate novel algorithms for robust UAV path planning in challenging urban settings.
  • To enhance the safety and efficiency of UAV operations through improved global path planning and local path adjustment.

Main Methods:

  • A branch-selected rapidly-exploring random tree (BS-RRT) algorithm was proposed for global path planning in environments with narrow passages.
  • A cyclic pruning algorithm was introduced to optimize the length of the planned paths.
  • An improved grey model (1,1) (RMGM(1,1)) was developed to predict the trajectories of dynamic flight obstacles for local path adjustments.

Main Results:

  • The BS-RRT algorithm exhibited superior convergence speed and stability in narrow passages compared to traditional RRT variants.
  • The cyclic pruning algorithm resulted in the shortest planned paths.
  • The RMGM(1,1) model demonstrated higher prediction accuracy for dynamic obstacle trajectories than existing models.

Conclusions:

  • The proposed BS-RRT algorithm and RMGM(1,1) model effectively address the complexities of UAV path planning in urban environments.
  • These advancements contribute to safer and more efficient autonomous flight operations for unmanned aerial vehicles.