Proposal of a New Model for Social Navigation Based on Extraction of Social Contexts from Ontology in Service Robots

With the growing presence of service robots in social environments, research into the development of social behavior in robots is becoming increasingly necessary. Service robotics and social navigation are research areas that have been growing in recent years. However, helping humans with everyday tasks as well as people who need special attention, the elderly and children are still challenging issues in both the domestic and commercial environments. In this paper we present the current problem of social navigation in service robots, a state-of-the-art review of social navigation models, and we propose a new social navigation model using context extraction from ontology. This is expected to improve both the naturalness and the sociability of the robot as well as the comfort of the human being. This project intends to use current detection and learning techniques and tools such as OpenPose, as well as semantic mapping and deep learning. Navigation experiments were performed in simulated environment and selection of a navigation method. Also started implementing people tracking using OpenPose with satisfactory preliminary results. It is hoped that with this project, we can collaborate with the field of social navigation research using ontology approach.