Fault tolerance analysis of decentralized swarm coordination under progressive agent failure in autonomous robotic systems

Decentralized multi-agent systems based on swarm intelligence offer inherent advantages in scalability and redundancy over centralized architectures. However, quantitative characterization of fault tolerance remains insufficiently studied. This paper presents a systematic experimental investigation of swarm degradation under controlled agent removal scenarios using a Reynolds-type flocking model with density-adaptive force modulation, implemented in Python 3.11 and subjected to progressive failure rates of 10 %-50 %. Three fault tolerance metrics are proposed: the Graceful Degradation Index ( G D I ), Network Connectivity Ratio (NCR), and Mission Completion Robustness (MCR). Both deterministic ( F 1 ) and stochastic Weibull-based ( F 2 ) failure models are evaluated across four redundancy levels ( ρ = 1.0-2.0) with 30 replications per cell. One-way ANOVA confirms statistically significant degradation effects ( F ( 5,714 ) = 1570.4, p < 0.001, η 2 = 0.917). The adaptive algorithm achieves MCR = 0.5 versus MCR = 0.3 for the baseline, maintaining GDI > 1.0 across all failure rates tested. These findings provide the first quantitative engineering-specification-style fault tolerance thresholds for Reynolds-type swarm coordination.