Estimation of Object Handover Position Using Human-Robot Proxemics and Unsupervised Pattern Recognition

Abstract

The increasing elderly population presents significant challenges in terms of the meeting of their daily care needs. Cognitive decline and reduced arm reflexes following balance loss impede the elderly’s execution of activities of daily living. To address these challenges, robots have emerged as valuable assistants for elderly individuals in their daily activities, including object manipulation, and have the potential to significantly improve the quality of life for the aging population. However, no research has been undertaken to enhance the selection of object handover locations in human-robot interactions by merging topology mapping with both parties’ range of motion, based on personal space. Based on the idea of personal space within human-robot proxemics, this research presents an alternative approach that makes use of topological mapping while taking into account the range of motion of both humans and robots. This research aims to minimize the expenses related to human-robot proximity and to determine the best locations for item handovers in order to discover which locations are optimal. In order to improve object handover locations, this work is a groundbreaking attempt to combine growing neural gas and human proxemics inside a robotic framework. Furthermore, it implies the creation of robot behaviors that resemble human proximity by estimating personal distances and incorporating rule-based requirements for item handover locations by taking into account the mobility ranges of both humans and robots. The simulation findings reported in this work show the ability of the suggested methodology and offer interesting information and prospects for further developments in the area of object handovers by robots.