Detecting and diagnosing navigational mistakes

Abstract

This paper looks at how to detect and diagnose mistakes autonomous mobile robots make while navigating through large-scale space using vision. Mistakes are perceptual, cognitive, or motor events that divert one from the intended route. Detection and diagnosis consist of realizing a mistake has occurred, determining what it was, and when it happened. This paper describes an approach that detects mistakes by finding mis-matches between observations and expectations. It diagnoses mistakes by examining knowledge from a variety of sources, including a history of observations and actions. It supports these operations by using symbolic visual information to compare expectations with observations augmented by a priori knowledge. This paper describes MUCKLE, the simulation used to test the approach, and presents experimental results that demonstrate its effectiveness.