Visual landmark learning

Publisher Website
Google Scholar
Abstract
Biology often offers valuable example of systems both for learning and for controlling motion. Work in ro- botics has often been inspired by these findings in di- verse ways. Though, the fundamental aspects that in- volve visual landmark learning and motion control mech- anisms have almost exclusively been approached heuris- tically rather than examining the underlying princi- ples. In this paper we introduce theoretical tools that might explain how the visual learning works and why the motion is attracted by the pre-learnt goal position. Basically, the theoretical tools emerge from the nav- igation vector field produced by the visual behaviors. Both the learning process and the navigation scheme influence the motion field. We apply classical mathe- matical and dynamic control to analyze the efficiency of our method.

This publication has 18 references indexed in Scilit: