On-line novelty detection for autonomous mobile robots
The use of mobile robots for inspection tasks is an attractive idea. A robot can travel through environments that humans cannot, and can be trained to identify sensor perceptions that signify potential or actual problems without requiring human intervention. However, in many cases, the appearance of a problem can vary widely, and ensuring that the robot does not miss any possible appearance of the problem (false negatives) is virtually impossible using conventional methods. This paper presents an alternative methodology using novelty detection. A neural network is trained to ignore normal perceptions that do not suggest any problems, so that anything that the robot has not sensed before is highlighted as a possible fault. This makes the incidence of false negatives less likely. We propose a novelty filter that can operate on-line, so that each new input is evaluated for novelty with respect to the data seen so far. The novelty filter learns to ignore inputs that have been sensed previously, or where similar inputs have been perceived. We demonstrate the use of the novelty filter on a series of simple inspection tasks using a mobile robot. The robot highlights those parts of an environment that are novel in some way, that is they are not part of the model acquired during exploration of a different environment. We show the effectiveness of the method using inputs from both sonar sensors and a monochrome camera.