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Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic

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Summary
This summary is machine-generated.

This study introduces a hybrid approach combining deterministic coordinated multi-robot exploration (CME) with the salp swarm algorithm (SSA) for efficient mapping in complex environments. The new CME-SSA method significantly improves exploration rates and reduces runtime compared to existing techniques.

Keywords:
algorithmcoordinated multi-robot explorationoptimizationrobot path planningsalp swarm algorithm

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multi-robot exploration (MRE) is crucial for mapping complex, obstacle-filled spaces.
  • Existing MRE methods often rely on deterministic or meta-heuristic algorithms, with limited integration of both.
  • Combining deterministic and meta-heuristic approaches can leverage their respective strengths for improved exploration.

Purpose of the Study:

  • To propose and evaluate a novel hybrid method for coordinated multi-robot exploration.
  • To enhance space search efficiency by integrating deterministic coordinated multi-robot exploration (CME) with the salp swarm algorithm (SSA).
  • To assess the performance of the proposed CME-SSA method against other established algorithms.

Main Methods:

  • A hybrid approach combining deterministic CME for cell precedence determination (cost and utility) with SSA for search space optimization.
  • Implementation of the CME-SSA algorithm for coordinated multi-robot mapping tasks.
  • Comparative analysis using performance metrics: runtime, explored area percentage, and completion success rate.

Main Results:

  • The proposed CME-SSA method demonstrated superior performance over CME-GWO, CME-GWOSSA, CME-SCA, and CME across seven diverse maps.
  • CME-SSA achieved a higher percentage of explored area in significantly less time.
  • Simulation results confirmed effective robot distribution and successful completion rates for the CME-SSA approach.

Conclusions:

  • The hybrid CME-SSA method offers a significant advancement in multi-robot exploration efficiency and effectiveness.
  • Integrating deterministic and meta-heuristic strategies provides a robust solution for complex mapping challenges.
  • The proposed method successfully optimizes robot coordination for superior exploration outcomes.