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Multi robot exploration using an advanced multi-objective salp swarm algorithm for efficient coverage and

Ali El Romeh1, Seyedali Mirjalili2,3,4

  • 1Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Melbourne, 3000, Australia.

Scientific Reports
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

The Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET) improves multi-robot exploration by balancing efficiency and accuracy. AMET outperforms existing methods in area coverage and coordination for complex tasks.

Keywords:
Area coverageArtificial IntelligenceAutonomous robotic systemsComputational efficiencyMulti-objective optimizationMulti-robot explorationSalp swarm algorithm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-robot exploration is crucial for tasks like search-and-rescue and environmental monitoring.
  • Existing methods often struggle to balance exploration efficiency with mapping accuracy.
  • Adaptive search strategies are needed to enhance robustness in complex environments.

Purpose of the Study:

  • To introduce the Advanced Multi-Objective Salp Swarm Algorithm Exploration Technique (AMET) for enhanced multi-robot exploration.
  • To develop a framework that integrates deterministic coordination with adaptive multi-objective optimization.
  • To improve the trade-off between exploration efficiency and mapping accuracy in multi-robot systems.

Main Methods:

  • AMET combines Coordinated Multi-Robot Exploration (CME) with the Multi-Objective Salp Swarm Algorithm (MSSA).
  • Performance was evaluated against various single-objective and multi-objective strategies (CME-MGWO, CME-MACO, CME-MODA, CME-SSA).
  • Key metrics included runtime efficiency, area coverage, mission resilience, and redundancy reduction.

Main Results:

  • AMET demonstrated superior performance across all evaluated metrics compared to existing methods.
  • The technique achieved significantly better area coverage and reduced computational overhead.
  • Enhanced exploration coordination and mission completion resilience were observed.

Conclusions:

  • AMET offers a scalable and efficient approach for multi-robot exploration.
  • The framework provides a robust solution for balancing exploration efficiency and mapping accuracy.
  • AMET has potential applications in search-and-rescue, planetary exploration, and environmental monitoring.