Bayesian Robot Programming

بهبود یادگیری رفتار روبات سیار دارای خطا در سنسورهای آن با استفاده از شبکه بیزین

In this paper a new structure based on Bayesian networks is presented to improve mobile robot behavior, in which there exist faulty robot sensors. If a robot likes to follow certain behavior in the environment to reach its goal, it must be capable of making inference and mapping based on prior knowledge and also should be capable of understanding its reactions on the environment over time. Old ...

Basic Concepts of Bayesian Programming

The purpose of this chapter is to introduce gently the basics concepts of Bayesian Programming. After a short formal introduction of Bayesian Programming, we present these concepts using three simple experiments with the mini mobile robot Khepera. These three instances have been selected among the numerous experiments done with this robots for their simplicity and didactic quality. A more exten...

A Bayesian framework for robotic programming

Abstract. We propose an original method for programming robots based on bayesian inference and learning. This method formally deals with problems of uncertainty and incomplete information that are inherent to the field. Indeed, the principal difficulties of robot programming comes from the unavoidable incompleteness of the models used. We present the formalism for describing a robotic task as w...

Cost Analysis of Acceptance Sampling Models Using Dynamic Programming and Bayesian Inference Considering Inspection Errors

Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...

Bayesian robots programming

Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples

Cognition and Reasoning with uncertain and partial knowledge is probably the biggest challenge for autonomous mobile robotics. Previous robotics systems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases t...