Distributed sensor analysis for fault detection in tightly-coupled multi-robot team tasks

This paper presents a distributed version of our previous work, called SAFDetection, which is a sensor analysis-based fault detection approach that is used to monitor tightly-coupled multi-robot team tasks.While the centralized version of SAFDetection was shown to be successful, a shortcoming of the approach is that it does not scale well to large team sizes. The distributed SAFDetection approach addresses this problem by adapting and distributing the approach across team members. Distributed SAFDetection has the same theoretic foundation as centralized SAFDetection, which maps selected robot sensor data to a robot state by using a clustering algorithm, and builds state transition diagrams to describe the normal behavior of the robot system. However, rather than processing multiple robots´ sensor data centralized on a server, distributed SAFDetection performs feature selection and clustering on individual robots to build the normal behavior model of an individual robot and the entire robot team. Fault detection is also accomplished in a distributed manner. We have implemented this distributed approach on a physical robot team and in simulation. This paper presents the results of these experiments, showing that distributed SAFDetection is an efficient approach to detect both local and interactive faults in tightly-coupled multi-robot team tasks. Compared to the centralized version, this approach provides more scalability and reliability.