Simulation framework of ubiquitous network environments for designing diverse network robots

Abstract

Smart homes provide residents with services that offer convenience using sensor networks and a variety of ubiquitous instruments. Network robots based on such networks can perform direct services for these residents. Information from various ubiquitous instruments and sensors located in smart homes is shared with network robots. These robots effectively help residents in their daily routine by accessing this information. However, the development of network robots in an actual environment requires significant time, space, labor, and money. A network robot that has not been fully developed may cause physical damage in unexpected situations. In this paper, we propose a framework that allows the design and simulation of network robot avatars and a variety of smart homes in a virtual environment to address the above problems. This framework activates a network robot avatar based on information obtained from various sensors mounted in the smart home; these sensors identify the daily routine of the human avatar residing in the smart home. Algorithms that include reinforcement learning and action planning are integrated to enable the network robot avatar to serve the human avatar. Further, this paper develops a network robot simulator to verify whether the network robot functions effectively using the framework.