Swarm Intelligence for Collaborative Play in Humanoid Soccer Teams
Humanoid soccer robots operate in dynamic, unpredictable, and often partially observable settings. Effective teamwork, sound decision-making, and real-time collaboration are critical to competitive performance. In this paper, a biologically inspired swarm-intelligence framework for humanoid soccer i...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/11/3496 |
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| Summary: | Humanoid soccer robots operate in dynamic, unpredictable, and often partially observable settings. Effective teamwork, sound decision-making, and real-time collaboration are critical to competitive performance. In this paper, a biologically inspired swarm-intelligence framework for humanoid soccer is proposed, comprising (1) a low-overhead communication User Datagram Protocol (UDP) optimized for minimal bandwidth and graceful degradation under packet loss; (2) an Ant Colony Optimization (ACO)-based decentralized role allocation mechanism that dynamically assigns attackers, midfielders, and defenders based on real-time pheromone trails and local fitness metrics; (3) a Reynolds’ flocking-based formation control scheme, modulated by role-specific weighting to ensure fluid transitions between offensive and defensive formations; and (4) an adaptive behavior layer integrating lightweight reinforcement signals and proactive failure-recovery strategies to maintain cohesion under robot dropouts. Simulations demonstrate a 25–40% increase in goals scored and an 8–10% boost in average ball possession compared to centralized baselines. |
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| ISSN: | 1424-8220 |