Constructing a cohesive pattern for collective navigation based on a swarm of robotics

Area of Science:

• Robotics
• Artificial Intelligence
• Distributed Systems

Background:

• Swarm robotics enables complex tasks through collective robot action.
• Robot aggregation is vital for swarm task completion but faces challenges like limited sensing, communication, and decentralized control.
• Existing aggregation algorithms struggle with no positional information and only local robot interactions.

Purpose of the Study:

• To propose a novel aggregation algorithm for swarm robotics systems.
• To address limitations of decentralized control, local information interaction, and lack of positional data in robot aggregation.
• To enhance collective navigation and recruitment strategies within swarm systems.

Main Methods:

• A new aggregation algorithm combining wave-based collective navigation and a recruitment strategy.
• The algorithm operates in two distinct phases: a searching phase and a surrounding phase.
• Execution time analysis and one-way analysis of variance were employed for performance evaluation.

Main Results:

• The proposed aggregation algorithm achieved a significant 41% reduction in aggregation time compared to existing methods.
• Experimental results demonstrated that increasing swarm size positively impacts overall group performance.
• Statistical analysis confirmed the significance of the observed performance improvements.

Conclusions:

• The novel aggregation algorithm effectively overcomes challenges in decentralized swarm robotics.
• The algorithm offers a substantial improvement in aggregation efficiency and performance.
• Future work may explore scalability and adaptability to diverse swarm robotics applications.