Knowledge-based intelligent diagnosis of ground robot collision with non detectable obstacles

The paper describes the application of fuzzy techniques to analyze motion problems in a mobile robot. The robot is equipped with ultrasound sensors used for obstacle detection, but, in some cases, small obstacles are out of the range of the sensors and can be dragged by the robot without being detected. Using other variables as, measured velocity, undershoots of that velocity or changes in battery voltage, a fuzzy system is able to determine those situations. The paper also analyzes the knowledge extraction process for the application using expert and induced knowledge (from data collected during navigation tasks) in a cooperative way, dealing with integration and simplification issues. The expert knowledge was used for describing the robot behaviour in order to identify the variables that should be used with the aim of detecting a collision of the vehicle against an undetected obstacle, as well as proposing a suitable recovery action. Data collected in real trials were used for inducing knowledge so as to complete and validate the expert knowledge. Both kind of knowledge were integrated in the final fuzzy-based system. The aim is to build up a knowledge base, which is interpretable and accurate at the same time, and it is used by our fuzzy system in order to solve the motion problems under consideration / Cet article décrit l'application d'un système d'inférence floue pour analyser les problèmes de motricité d'un robot mobile. Il s'agit de détecter, à l'aide de variables de commande et de mesure telles que la vitesse, les obtacles trop petits pour être signalés par les capteurs ultrasons. Le processus d'extraxction des connaissances experte et induite est détaillé. Les deux types de connaissance sont ensuite intégrés au sein d'une même base de connaissance. Le système doit donc être à la fois précis, dans la détection des obstacles, et interprétable