Using supervised learning to fuse sensor data for surface tracking in complex environments
Kelly A. Korzeniowski · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
This paper is concerned with the problem of optimizing surface following control in automated systems. Tracking a surface is an integral task for many autonomous system. It can be used for navigation, surface preparation or object recognition. There are two types of control for surface following, continuous and discontinuous. The robot may maintain contact and continuously track the surface or touch the surface at discontinuous points. A balance is sought between each surface tracking method in the path planning phase, in order that the whole process be optimized in terms of time to complete the task and the amount of data collected. The tracking method is computed by the tracking algorithm using the partial data sets provided by sensors. It is common practice to outfit automated systems with the ability to gather data from many sensors. As the environmental conditions change, sensor reliability changes. Thus, the system's reliance on sensor data must also change. This work focuses on the addition of the supervisory learning module for choosing the method of surface tracking.