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Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots.

Qiuyu Cui1, Pengfei Liu1, Hualong Du1

  • 1School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Anshan, Liaoning, China.

Frontiers in Neurorobotics
|June 16, 2023
PubMed
Summary
This summary is machine-generated.

An improved multi-objective artificial bee colony algorithm (IMOABC) enhances mobile robot path planning. This bio-inspired approach offers superior performance over existing methods for complex optimization tasks and robot navigation.

Keywords:
Bio-inspired algorithmmobile robotsmulti-objective artificial bee colony algorithmmulti-objective optimizationpath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Mobile robots are crucial in diverse applications like space exploration, logistics, and rescue operations.
  • Efficient path planning is fundamental for mobile robot task completion and operational success.
  • Existing path planning algorithms face challenges in finding optimal routes for complex scenarios.

Purpose of the Study:

  • To develop an advanced bio-inspired algorithm for mobile robot path planning.
  • To enhance the multi-objective artificial bee colony algorithm (MOABC) for improved optimization capabilities.
  • To validate the efficacy of the proposed algorithm in complex multi-objective optimization problems and robot path planning simulations.

Main Methods:

  • Development of the improved multi-objective artificial bee colony algorithm (IMOABC) incorporating four novel strategies: external archive pruning, non-dominated ranking, crowding distance, and an enhanced search strategy.
  • Testing and validation of the IMOABC algorithm on six standard multi-objective test functions.
  • Application and comparative analysis of the IMOABC algorithm for mobile robot path planning in simulation environments against established algorithms like MOABC and the basic artificial bee colony (ABC) algorithm.

Main Results:

  • The IMOABC algorithm demonstrated superior performance in solving complex multi-objective optimization problems compared to other tested algorithms.
  • In mobile robot path planning simulations, the IMOABC algorithm consistently outperformed both the MOABC and ABC algorithms.
  • The enhanced strategies within IMOABC effectively improved its ability to find optimal paths in challenging environments.

Conclusions:

  • The developed IMOABC algorithm represents a significant advancement in bio-inspired path planning for mobile robots.
  • IMOABC offers a robust and efficient solution for complex multi-objective optimization problems relevant to robotics.
  • This algorithm shows broad applicability and potential for improving the operational capabilities of mobile robots across various domains.