An Economic Simultaneous Localization and Mapping System for Remote Mobile Robot Using SONAR and an Innovative AI Algorithm

This paper proposes an economic and effective approach towards the simultaneous localization and mapping of a mobile rescue robot using a single ultrasonic range finder. The procedure eliminates the complication involved with localizing the robot in a map while creating the map simultaneously, by employing a novel control mechanism. The problem is solved by separating the mapping and localization processes and merging the outputs after specific intervals. The data from sensory devices is processed wirelessly to map the surroundings of the robot on a computer. The innovative AI algorithm extends the map cognitively as subsequent data is acquired. Moreover, a comparison is made between conventional methodologies and the one presented in this paper along with possible enhancements applicable. Using this architecture, the economic cost, computational power and time required for SLAM are significantly reduced. The design was utilized on a mobile rescue robot for the RoboCup competitions.