用仿生抗体模型检测机器人集群的渐进式故障，提升长期自主性。
Detecting and Diagnosing Faults in Autonomous Robot Swarms with an Artificial Antibody Population Model
- 基于免疫机制设计仿生抗体群体，监测集群性能缓慢退化。
- 在多种规模集群中实现70%~97%的性能保持，有效防止现场故障。
- 支持分布式部署与有监督/无监督模式，适合大规模机器人系统。
主动容错（包括故障检测、诊断与恢复）对实现机器人长期自主运行至关重要，尤其在多机器人系统和集群中。以往工作主要关注少数机器人传感器与执行器的突发性电机械故障，但未充分考虑持续运行导致的渐进式磨损，这类故障更难检测。本文提出人工抗体群体动力学（AAPD）模型——一种受免疫系统启发的方法，用于检测和诊断机器人集群的渐进性退化。实验表明，该模型能可靠识别渐进性退化及突发性能变化，在不同规模的机器人集群中均表现出对正常行为机器人的鲁棒性。模型为分布式架构，支持监督与非监督配置，具备良好可扩展性。在经历渐进退化的觅食机器人集群中部署后，系统平均维持了理想条件下70%至97%的性能，并在多数测试场景中避免了现场机器人失效。
原文摘要 · Abstract (English)
An active approach to fault tolerance, the combined processes of fault detection, diagnosis, and recovery, is essential for long term autonomy in robots -- particularly multi-robot systems and swarms. Previous efforts have primarily focussed on spontaneously occurring electro-mechanical failures in the sensors and actuators of a minority sub-population of robots. While the systems that enable this function are valuable, they have not yet considered that many failures arise from gradual wear and tear with continued operation, and that this may be more challenging to detect than sudden step changes in performance. This paper presents the Artificial Antibody Population Dynamics (AAPD) model -- an immune-inspired model for the detection and diagnosis of gradual degradation in robot swarms. The AAPD model is demonstrated to reliably detect and diagnose gradual degradation, as well as spontaneous changes in performance, among swarms of robots of varying sizes while remaining tolerant of normally behaving robots. The AAPD model is distributed, offers supervised and unsupervised configurations, and demonstrates promising scalable properties. Deploying the AAPD model on a swarm of foraging robots undergoing gradual degradation enables the swarm to operate on average at between 70% - 97% of its performance in perfect conditions and is able to prevent instances of robots failing in the field during experiments in most of the cases tested.
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