Mobile Robot Fault Detection based on Redundant Information Statistics
Any type of content formally published in an academic journal, usually following a peer-review process.
Detecting and reacting to faults (i.e., abnormal situations) are essential skills for robots to safely and autonomously perform tasks in human-populated environments. This paper presents a fault detection algorithm that statistically monitors robot motion execution. The algorithm does not model possible motion faults, but it instead uses a model of normal execution to detect anomalies. The model of normal execution is based on comparisons between redundant sources of information; specifically, wheel encoder readings and localization algorithm output are used as the redundant sources of displacement information. The algorithm was implemented on a service robot that often navigates in a human-populated environment without supervision. Experimental results show that the algorithm can detect even minor motion faults and stop execution immediately to guarantee safety to the humans around the robot.