RUN: rational ubiquitous navigation, a model for automated navigation and searching in virtual environments

Abstract

By now, the realm of virtual reality is abuzz with high-quality visuals, enough to simulate a real-world scene. The use of intelligence in virtual reality systems, however, is a milestone yet to be achieved to make possible seamless realism in a virtual environment. This paper presents a model, rational ubiquitous navigation to improve believability of a virtual environment. The model intends to augment maturity of a virtual agent by inculcating in it the human-like learning capability. A novel approach for automated navigation and searching is proposed by incorporating machine learning in virtual reality. An intelligent virtual agent learns objects of interest along with the paths followed for navigation. A mental map is molded dynamically as a user navigates in the environment. The map is followed by the agent during self-directed navigation to access any known object. After reaching at a location where an object of interest resides, the required object is selected on the basis of front-facet feature. The model is implemented in a case-study project learn objects on path (LOOP). Twelve users evaluated the model in the immersive maze-like environment of LOOP. Results of the evaluation assure applicability of the model in various cross-modality applications.