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A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning.

Hui Ren1,2,3, Luli Gao1,2,3, Xiaochen Shen1,2,3

  • 1School of Information and Communication Engineering, Communication University of China, No.1 Dingfuzhuang East Street, Chaoyang District, Beijing 100024, China.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

A novel Para-PSO-ABC algorithm enhances mobile robot path planning by mimicking parasitic relationships for improved safety and cost-efficiency. Its staged evolutionary approach optimizes navigation in complex environments.

Keywords:
Para-PSO-ABC algorithmdual-community-evolutionary structuremulti-swarm evolutionparasitic relationshippath planning

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Mobile robot path planning is crucial for autonomous systems, facing challenges in safety and cost-efficiency.
  • Existing algorithms often struggle with complex environments and dynamic obstacle avoidance.
  • Bio-inspired optimization strategies offer potential for enhanced path planning solutions.

Purpose of the Study:

  • To introduce a new multi-swarm-evolutionary algorithm, Para-PSO-ABC, for mobile robot path planning.
  • To enhance path safety and reduce operational costs by integrating parasitic relationships.
  • To improve navigation efficiency in environments with varying obstacle configurations.

Main Methods:

  • Developed the Para-PSO-ABC algorithm, combining modified particle swarm optimization (MPSO) and artificial bee colony (ABC) algorithms.
  • Implemented a three-stage evolutionary structure: independent, fusion, and interaction stages.
  • Incorporated a multimodal routing strategy, segment obstacle-avoiding strategy, and multi-swarm-elite filtering.

Main Results:

  • The Para-PSO-ABC algorithm demonstrated effectiveness in enhancing safety and cost-efficiency for mobile robot path planning.
  • The parasitism-relation-based structure and stage-based evolution strategy proved productive in optimizing path selection.
  • Simulations in real routing environments with diverse obstacle allocations validated the algorithm's performance against typical methods.

Conclusions:

  • The proposed Para-PSO-ABC algorithm offers a robust and efficient solution for mobile robot path planning.
  • The bio-inspired, multi-stage evolutionary approach significantly improves navigation safety and cost-effectiveness.
  • This study highlights the potential of ecological relationships in developing advanced AI-driven optimization techniques.