预测性维护让机器人集群提前防故障，提升长期可靠性。
Anticipating Degradation: A Predictive Approach to Fault Tolerance in Robot Swarms
- 通过预测潜在故障提前干预，而非等故障发生后才处理。
- 测试显示该方法在几乎所有场景下表现优于传统被动响应方式。
- 适合需要长期自主运行的机器人集群系统应用。
实现机器人集群的长期自主运行，主动式容错机制至关重要。以往研究多关注突发性的电-机械故障与失效问题，但许多故障是随时间渐进发生的。若等到故障显现出失败迹象才处理，则在多种场景下效率低下且不可持续。本文提出，借鉴预测性维护理念，在故障影响集群运行前即加以解决，是一种实现长期容错的可行新路径。实验表明，该方法在几乎所有测试场景中均表现出可比或更优的性能，显著优于传统的反应式容错策略。
原文摘要 · Abstract (English)
An active approach to fault tolerance is essential for robot swarms to achieve long-term autonomy. Previous efforts have focused on responding to spontaneous electro-mechanical faults and failures. However, many faults occur gradually over time. Waiting until such faults have manifested as failures before addressing them is both inefficient and unsustainable in a variety of scenarios. This work argues that the principles of predictive maintenance, in which potential faults are resolved before they hinder the operation of the swarm, offer a promising means of achieving long-term fault tolerance. This is a novel approach to swarm fault tolerance, which is shown to give a comparable or improved performance when tested against a reactive approach in almost all cases tested.
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