A Knowledge Interchange Format (KIF) for Robots in Cloud

Abstract

The most important factor to increase knowledge is to share what is learned. This approach of sharing what is learned is incorporated in robotics so that the robots can solve day to day problems by learning when required. The proposed knowledge interchange format (KIF) deals with communication amongst heterogeneous robots through the cloud. Keeping in mind the size of data, the emphasis has given to generate a representation of the path for robots that can be processed parallelly and saved in the cloud, enabling the sharing of knowledge between robots. In order to achieve this compatibility, a path as a set of characters or a string is considered with a convention that is predefined so that no matter how much the robotics evolves, it can always interpret a string. The string is tested in a Hadoop map-reduce program to process the paths parallelly and provide the best path from the cloud.