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A UAV path planning method based on the framework of multi-objective jellyfish search algorithm.

Xia Wang1,2, Yaning Feng3,4, Jianing Tang5,6

  • 1Yunnan Key Laboratory of Unmanned Autonomous System, Yunnan Minzu University, Kunming, 650504, China. wangxiacsu@163.com.

Scientific Reports
|November 14, 2024
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Summary
This summary is machine-generated.

This study introduces an improved Unmanned Aerial Vehicle (UAV) path planning method using the multi-objective jellyfish search algorithm (UMOJS). The enhanced UMOJS algorithm generates higher-quality initial paths and improves convergence for complex, many-objective optimization problems.

Keywords:
Class-optimal individualMany-objective optimizationMulti-objective jellyfish searchRapidly-exploring Random TreesUAV path planning

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Multi-constraint Unmanned Aerial Vehicle (UAV) path planning is a complex many-objective optimization challenge.
  • Existing meta-heuristic algorithms suffer from low-quality initial paths and poor convergence with increasing objectives.
  • This necessitates improved algorithms for efficient and effective UAV path planning.

Purpose of the Study:

  • To propose a novel UAV path planning method, the multi-objective jellyfish search algorithm (UMOJS), to address limitations of existing approaches.
  • To enhance initial path quality and improve convergence capabilities in many-objective optimization scenarios.
  • To achieve superior performance in terms of path length and solution distribution for UAVs.

Main Methods:

  • The proposed UMOJS algorithm integrates a Rapidly-exploring Random Trees (RRT) based initialization strategy for higher-quality initial paths.
  • A class-optimal individual-guided jellyfish updating strategy is employed to boost algorithm convergence.
  • Predefined reference points are utilized to obtain Pareto optimal solutions with improved convergence and distribution.

Main Results:

  • The UMOJS algorithm demonstrated the ability to generate more UAV paths with shorter lengths.
  • Experimental results showed more evenly distributed Pareto optimal solutions compared to five other meta-heuristic algorithms.
  • The proposed method effectively satisfies constraint conditions in simulated flight environments of varying difficulties.

Conclusions:

  • The UMOJS algorithm offers a superior approach to multi-constraint UAV path planning, particularly for many-objective problems.
  • The integration of RRT initialization and class-optimal guided updates significantly enhances performance.
  • The method provides a promising solution for generating efficient and well-distributed UAV flight paths.