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Related Experiment Video

Updated: Sep 10, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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Hybrid golden Jackal and moth flame optimization algorithm based coverage path planning in heterogeneous UAV

K Karthik1, C Balasubramanian2, R Praveen3

  • 1Department of Electronics and Communication Engineering, P.S.R.R. College of Engineering, Sivakasi, Tamil Nadu, 626140, India. karthikkesavan@psrr.edu.in.

Scientific Reports
|August 23, 2025
PubMed
Summary

This study introduces a hybrid optimization algorithm for Unmanned Aerial Vehicle (UAV) coverage path planning. The novel approach enhances efficiency, reducing task completion and execution times for comprehensive region coverage.

Keywords:
Coverage path planningGolden Jackal optimization algorithmMoth flame optimization algorithmRegion of interestUnmanned aerial vehicle (UAV)

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Operations Research

Background:

  • Coverage Path Planning (CPP) is crucial for Unmanned Aerial Vehicles (UAVs) to efficiently survey target regions.
  • Optimizing flight paths for heterogeneous UAVs with varying capabilities presents significant challenges.
  • Minimizing energy consumption and task completion time are key objectives in UAV-based coverage missions.

Purpose of the Study:

  • To propose a novel hybrid optimization algorithm for effective Coverage Path Planning (CPP) using heterogeneous UAVs.
  • To enhance the efficiency and efficacy of UAVs in comprehensively covering designated target regions.
  • To optimize flight paths for reduced energy consumption and improved flight performance.

Main Methods:

  • Development of a hybrid Golden Jackal and Moth Flame Optimization Algorithm (HGJMFOA) for CPP.
  • Integration of a linear programming model for identifying optimal point-to-point flight paths for individual UAVs.
  • Exploration and exploitation of feasible paths using HGJMFOA to determine shortest paths and minimize flight time.

Main Results:

  • The HGJMFOA approach demonstrated significant improvements in performance metrics.
  • Minimized task completion time by 21.34%.
  • Reduced deviation ratio by 24.56% and execution time by 34.19%.

Conclusions:

  • The proposed HGJMFOA strategy effectively optimizes coverage path planning for heterogeneous UAVs.
  • Cooperation between diversified UAVs achieved optimal coverage, sensor range utilization, and superior flight performance.
  • Simulation results validate the algorithm's efficacy in minimizing time and improving overall mission efficiency.