Efficient object motion prediction using fuzzy Petri net based modelling in a robot navigational environment
by Vijay S. Rajpurohit; M.M. Manohara Pai
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 10, No. 1/2, 2012

Abstract: Predicting the next instance position of a moving object in a dynamic navigational environment is a critical issue as it involves uncertainty. This paper proposes a fuzzy rule-based motion prediction algorithm for predicting the next instance position of a moving object. The algorithm is robust in handling the uncertain data of real-life situation. The fuzzy rule base modeling is done using Fuzzy Petri Net (FPN) formalism. The prediction algorithm is tested for real-life bench-marked data sets and compared with existing motion prediction techniques. The performance of the algorithm is comparable to the existing prediction methods.

Online publication date: Wed, 31-Dec-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Vehicle Autonomous Systems (IJVAS):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com