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Related Experiment Video

Updated: Feb 14, 2026

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MURM-A*: An Improved A* Within Comprehensive Path-Planning Scheme for Cellular-Connected Multi-UAVs Based on Radio

Yanming Chai1, Qibin He1, Yapeng Wang1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MURM-A*, an improved A* algorithm for multiple cellular-connected Unmanned Aerial Vehicles (UAVs). It enhances path planning by integrating radio maps and complex networks, ensuring flight safety and network connectivity in urban airspace.

Keywords:
A-starcomplex networkmulti-UAVpath planningpath-planning modelradio mapunmanned aerial vehicle (UAV)

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Area of Science:

  • Robotics and Autonomous Systems
  • Network Engineering
  • Aerospace Engineering

Background:

  • Cellular-connected Unmanned Aerial Vehicles (UAVs) require persistent network connectivity and flight safety in dense urban airspace.
  • Existing path-planning methods struggle with environmental data processing, obstacle avoidance, flight dynamics, and multi-UAV conflict resolution.
  • Traditional A* algorithm limitations hinder simultaneous optimization of path efficiency and radio quality for UAVs.

Purpose of the Study:

  • To develop a comprehensive path-planning scheme for multiple cellular-connected UAVs.
  • To address limitations in existing research regarding environmental map data processing and multi-constraint path planning.
  • To improve flight safety and network connectivity for UAVs in complex urban radio environments.

Main Methods:

  • Constructed a path-planning model using complex-network theory, environmental data, and radio maps.
  • Developed an improved A* algorithm (MURM-A*) for multi-UAV scenarios.
  • Separated environmental representation from algorithmic search for enhanced processing.

Main Results:

  • The MURM-A* algorithm effectively avoids obstacles and prevents spatial conflicts between UAV paths.
  • Achieved joint optimization of path efficiency and radio quality, reducing radio-outage time compared to Deep Reinforcement Learning (DRL).
  • The path-planning model improved environmental information identification and reduced modeling time compared to DRL.

Conclusions:

  • The proposed systematic framework provides reliable path planning for multiple cellular-connected UAVs in complex radio environments.
  • MURM-A* offers a valid and efficient alternative to traditional A* and DRL methods for UAV path planning.
  • The study establishes a well-structured and extensible framework for future UAV path-planning research.