  In swarm robotics, any of the robots in a swarm may be affected by different
faults, resulting in significant performance declines. To allow fault recovery
from randomly injected faults to different robots in a swarm, a model-free
approach may be preferable due to the accumulation of faults in models and the
difficulty to predict the behaviour of neighbouring robots. One model-free
approach to fault recovery involves two phases: during simulation, a
quality-diversity algorithm evolves a behaviourally diverse archive of
controllers; during the target application, a search for the best controller is
initiated after fault injection. In quality-diversity algorithms, the choice of
the behavioural descriptor is a key design choice that determines the quality
of the evolved archives, and therefore the fault recovery performance. Although
the environment is an important determinant of behaviour, the impact of
environmental diversity is often ignored in the choice of a suitable
behavioural descriptor. This study compares different behavioural descriptors,
including two generic descriptors that work on a wide range of tasks, one
hand-coded descriptor which fits the domain of interest, and one novel type of
descriptor based on environmental diversity, which we call
Quality-Environment-Diversity (QED). Results demonstrate that the
above-mentioned model-free approach to fault recovery is feasible in the
context of swarm robotics, reducing the fault impact by a factor 2-3. Further,
the environmental diversity obtained with QED yields a unique behavioural
diversity profile that allows it to recover from high-impact faults.

Neural and Evolutionary Computing