Spatiotemporal Motion Profiles for Cost-Based Optimal Approaching Pose Estimation

Abstract

Recent public perceptions indicate a positive shift towards a society with human and robot co-existing, especially aged populations. The ability to socially navigate become crucial for mobile robots by enabling them to guarantee not only human physical safety but also psychological comfort, and enhance robots contextual awareness in human-robot interactions (HRI). In this study, we introduce an extended navigation scheme to approach moving target based on the tracking of human spatiotemporal motion, social studies on proxemics, and kino-dynamics of the mobile robot. The strategy utilizes existing multi-layer cost-based navigation mapping for complete integration with plannings and introduce soft social constraints by extending the costmap value range. The primary contributions include (i) spatio-temporal motion profiles (SMPs) of all humans under tracking, (ii) a social navigation cost function (SNCF) for filtering socially-optimal goal poses. The results drawn from simulated testings across three normative social situations, and statistical analysis demonstrate the SMPs effectiveness through measured spatial and temporal coefficients. The driving factors safety and appropriate social construct are determined to be either statistically or practically significant, while also introducing a complete navigation scheme taking into account of socially acceptable behaviours for the robot.