A comprehensive study and analysis of artificial intelligence-based waiter robot in restaurant

Abstract

"The rapid development of technology has led to the implementation of numerous solutions aimed at streamlining processes, one of which is the incorporation of artifi cial intelligence. The Simultaneous Localization and Mapping (SLAM) algorithm is fundamental to the restaurant robots operation and thereby determines its success or failure in carrying out its tasks. Few studies have looked at how well SLAM algo rithm work when combined with path planning for indoor location, even though the present two-dimensional Lidar-based SLAM algorithm has done quite well, especially in indoor scenarios. Planning and mapping routes for restaurant robots operating in an indoor setting is the topic of the following essay. The goal of this research is to find out how indoor location systems may make use of path planning algorithms in conjunction with SLAM methods. To verify the mapping data, real-time path planning must be investigated. For global path planning, the A* algorithm is used to find the most efficient route while avoiding obstacles. Local path planning makes use of the Dynamic window approach (DWA) algorithm. After extensive testing in simulated, emulated, and competitive indoor situations, it was determined that both the SLAM and path planning algorithms performed admirably. Further, we employ a speech recognition component to facilitate communication with clients and an ob ject identification model to track down lost items. Finally, experts may find this papers results useful when deciding which algorithms to use when building SLAM systems that meet their specific needs."