Measuring Resilience in Collective Robotic Algorithms

Measuring and comparing resilience is crucial for evaluating different algorithms' performance. Existing resilience metrics focus on a system's ability to maintain a particular state, but are inadequate to evaluate whether a system can achieve a novel state after an unexpected disturbance. The presented resilience power metric is used to analyze two best-of-N algorithms. Both algorithms exhibited high resilience power when changing the collective's population size, but this result did not correlate with high overall task performance.