An Investigation of Graceful Degradation in Boolean Network Robots Subject to Online Adaptation

The ability to resist to faults is a desired property in robotic systems. However, it is also hard to obtain, having to modify the behavior to face breakdowns. In this work we investigate the robustness against sensor faults in robots controlled by Boolean networks. These robots are subject to online adaptation-i.e., they adapt some structural properties while they actually act-for improving their performance at a simple task, namely phototaxis. We study their performance variation according to the number of faulty light sensors. The outcome is that Boolean network robots exhibit graceful degradation, as the performance decreases gently with the number of faulty sensors. We also observed that a moderate number of faulty sensors-especially if located contiguously-not only produces a negligible performance decrease, but it can be sometimes even beneficial. We argue that online adaptation is a key concept to achieve fault tolerance, allowing the robot to exploit the redundancy of sensor signals and properly tune the dynamics of the same Boolean network depending on the specific working sensor configuration.