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Multi-UAV Path Planning for Autonomous Missions in Mixed GNSS Coverage Scenarios.

Flavia Causa1, Giancarmine Fasano2, Michele Grassi3

  • 1Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy. flavia.causa@unina.it.

Sensors (Basel, Switzerland)
|December 2, 2018
PubMed
Summary

This study introduces a new algorithm for multiple unmanned aerial vehicles (UAVs) to plan paths in areas with varying Global Navigation Satellite Systems (GNSS) coverage. The system efficiently manages cooperative and independent flight, reducing mission time and computational load.

Keywords:
GNSS-challenging environmentcooperative navigationinsertion-based techniquesmulti-UAV path planningpolynomial path

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Area of Science:

  • Robotics
  • Aerospace Engineering
  • Computer Science

Background:

  • Navigation in challenging environments like urban canyons requires robust strategies for unmanned aerial vehicles (UAVs).
  • Heterogeneous Global Navigation Satellite Systems (GNSS) coverage presents unique challenges for multi-UAV coordination and path planning.
  • Existing methods may not efficiently adapt to dynamic changes in navigation conditions.

Purpose of the Study:

  • To develop an adaptive algorithm for multi-UAV path planning in environments with mixed GNSS availability.
  • To enable autonomous reconfiguration of multi-UAV systems based on real-time mission requirements and environmental conditions.
  • To optimize flight trajectories for efficient coexistence and reduced mission time.

Main Methods:

  • Formulating path planning as a vehicle routing problem to generate smooth, polynomial trajectories.
  • Estimating flight times to ensure safe UAV coexistence in GNSS-degraded areas.
  • Implementing a distributed system that autonomously adapts to varying navigation conditions.
  • Testing the algorithm in a realistic 3D simulation environment with variable numbers of UAVs and waypoints.

Main Results:

  • The algorithm demonstrates computational efficiency, with burden largely independent of the number of UAVs.
  • Near real-time implementation is feasible, even with a significant number of waypoints.
  • The solution effectively utilizes available flight resources, reducing overall mission duration.
  • The system successfully manages cooperative and independent flight modes based on GNSS availability.

Conclusions:

  • The proposed algorithm offers an efficient and adaptive solution for multi-UAV path planning in heterogeneous GNSS environments.
  • It provides a robust framework for autonomous system reconfiguration, enhancing operational flexibility.
  • The method optimizes resource utilization and mission time, making it suitable for practical applications.