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Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search

Hao-Xiang Chen1, Ying Nan2, Yi Yang3

  • 1Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China. chenhaoxiang@nuaa.edu.cn.

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Summary
This summary is machine-generated.

This study optimizes unmanned aerial vehicle (UAV) reconnaissance missions using a modified Multi-Objective Symbiotic Organisms Search (MOSOS) algorithm. The enhanced MOSOS efficiently solves the Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP) for improved task assignment.

Keywords:
Pareto dominance determinationUAVreconnaissance task assignmentsensorsymbiotic organisms search

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Area of Science:

  • Robotics and Automation
  • Operations Research
  • Artificial Intelligence

Background:

  • Reconnaissance missions require efficient task assignment for multiple heterogeneous unmanned aerial vehicles (UAVs).
  • Existing algorithms may not adequately address the complexities of time-window constraints and heterogeneous sensor capacities in UAV task assignment.

Purpose of the Study:

  • To develop an optimized approach for UAV task assignment in reconnaissance missions.
  • To address the Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP) for heterogeneous UAVs.

Main Methods:

  • A modified Multi-Objective Symbiotic Organisms Search (MOSOS) algorithm is proposed.
  • A time-window based task model for heterogeneous targets is established.
  • Double-chain encoding rules and Pareto dominance determination are incorporated.

Main Results:

  • The modified MOSOS algorithm demonstrates superior performance compared to the original MOSOS and NSGA-II.
  • Optimality and efficiency in solving the MTWDTSP for UAV task assignment are significantly improved.
  • Numerical and Monte-Carlo simulations validate the proposed method's effectiveness.

Conclusions:

  • The enhanced MOSOS algorithm provides an effective solution for complex UAV reconnaissance task assignment problems.
  • The method offers improved optimality and efficiency for heterogeneous UAVs operating under time constraints.
  • This research contributes to advancing autonomous mission planning for multi-UAV systems.