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

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.