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Area of Science:

  • Robotics
  • Autonomous Navigation
  • Path Planning

Background:

  • Path planning is crucial for robotics, enabling autonomous navigation in diverse fields like aerospace and agriculture.
  • Coverage Path Planning (CPP) specifically focuses on generating collision-free paths to cover entire areas.
  • Unmanned Aerial Vehicles (UAVs) require efficient path planning for tasks involving area coverage.

Purpose of the Study:

  • To present a novel 3D dynamic coverage path planning (3DD-CPP) method for UAVs.
  • To enable path planning in unknown 3D environments by combining linear optimization and heuristics.
  • To address the computational demands of on-board path planning through fog-edge computing.

Main Methods:

  • The 3DD-CPP method utilizes a combination of linear optimization and heuristic algorithms.
  • A cost matrix model was developed to estimate UAV power consumption at various flight speeds.
  • A distributed execution strategy using fog-edge computing was proposed to manage computational load.

Main Results:

  • The 3DD-CPP method demonstrated effective performance in simulated scenarios for both local and fog-edge execution.
  • The proposed heuristic approach allows for re-optimization, supporting execution with local environmental awareness.
  • The cost matrix model provided valuable insights into UAV power usage during path planning.

Conclusions:

  • The 3DD-CPP method offers a viable solution for autonomous navigation and area coverage tasks for UAVs in complex environments.
  • Distributed computing via fog-edge enhances the feasibility of complex path planning algorithms on UAVs.
  • The re-optimizing heuristic ensures adaptability and efficiency in dynamic or partially known environments.