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An adaptive A-Star algorithm to handle blood transportation using UAVs.

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This study optimizes drone delivery for medical samples by integrating 3D pathfinding with charging station placement. The approach ensures efficient, feasible drone logistics, overcoming traffic delays and battery limitations for critical healthcare transport.

Keywords:
Blood transportationDronesGISHealthcareUAV

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Area of Science:

  • Logistics and Supply Chain Management
  • Robotics and Autonomous Systems
  • Healthcare Operations Research

Background:

  • Timely medical sample transportation is vital, especially in congested urban areas where road delivery is slow.
  • Unmanned aerial vehicle (UAV) range is limited by battery life and variable energy use due to terrain.
  • Existing transportation methods face challenges with efficiency and speed in complex environments.

Purpose of the Study:

  • To develop an integrated system for optimizing drone-based medical sample transportation.
  • To enhance the efficiency and feasibility of drone logistics by addressing range and energy constraints.
  • To strategically deploy drone charging stations for improved operational coverage.

Main Methods:

  • Employed a 3D enhanced A* pathfinding algorithm to accurately estimate flight costs, considering elevation and obstacles.
  • Utilized K-means clustering for strategic placement of drone charging stations.
  • Integrated factors like population density, topography, power infrastructure, and drone power models into station placement.

Main Results:

  • Demonstrated significant minimization of flight distance and energy consumption in a Lebanese hospital case study.
  • Validated the mission feasibility of the proposed system under real-world operational constraints.
  • Showcased the effectiveness of the integrated approach in overcoming drone operational limitations.

Conclusions:

  • The proposed integrated system effectively optimizes drone-based medical transportation.
  • Combining advanced pathfinding with strategic charging station deployment enhances efficiency and overcomes operational challenges.
  • This approach offers a viable solution for improving medical logistics in challenging environments.