Proactive–reactive detection and mitigation of intermittent faults in robot swarms

This dataset accompanies an article submission. Abstract: Intermittent faults are transient errors that sporadically appear and disappear. Although intermittent faults pose substantial challenges to reliability and coordination, existing studies of fault tolerance in robot swarms focus instead on permanent faults. To address intermittent faults in robot swarms that have persistent networks, we propose a novel proactive–reactive strategy to detection and mitigation, based on self-organized backup layers and distributed consensus in a multiplex network. Proactively, robots self-organize dynamic backup paths before faults occur, adapting to changes in the primary network topology and the robots’ relative positions. Reactively, robots use one-shot likelihood ratio tests to compare information received along different paths in the multiplex network, enabling early fault detection. Upon detection, communication is temporarily rerouted in a self-organized way, until the detected fault resolves. We validate the approach in representative formation control scenarios, demonstrating that intermittent faults are prevented from disrupting convergence to desired formations, with high detection accuracy and low rates of false positives.