A Local Edge Companion Architecture for Human-Aware Communication and Service Augmentation in Cloud-Controlled Service Robots

Commercial cloud-controlled service robots are increasingly deployed in shared public facilities, yet many remain task-centric delivery platforms that cannot be easily extended with surrounding-people recognition, situated communication policy, or continuous access to internal middleware. This article proposes the Local Safety Companion Robot Extension (LSCRx), a non-invasive local edge companion architecture that augments such robots with human-aware communication while preserving manufacturer-controlled cloud operation and built-in safety functions. The central contribution is a communication-space design method that interprets robot–user distance, distance dynamics, body cues, field-of-view availability, and recognition confidence as service-level evidence. In the proposed conceptual policy, a gradual, confidence-supported decrease in distance would be treated as participation evidence, whereas separation, stopping outside the service zone, target loss, or unstable recognition would lead to waiting, distant greeting, information display, or other distance-preserving service actions. A prototype combines a commercial service robot with smartphone-class companion modules and a local edge server to demonstrate local body tracking, self-localization, data transmission, and cloud-API command preparation. Viewpoint-aware selection among registered spatial service waypoints (RSSWs) and anti-chasing rules are proposed as a conceptual model and were not implemented or evaluated in the present prototype. In the baseline route study, offline translational correction using known route-endpoint coordinates reduced the mean return-segment human-position error from 0.770 m to 0.476 m (approximately 38.2%) on the recorded data; this reduction was not independently validated. The evaluation was limited to one robot at a single university indoor location. Exploratory experiments involved seven participants, yielding six valid body-tracking datasets and seven questionnaire responses. These experiments and field observations suggest that contactless cues, camera placement, stop distance, and field of view jointly influence service usability under the tested conditions. The findings suggest a potential HRI design gap: the distance that is socially appropriate for a person may not coincide with the distance that is technically appropriate for reliable skeleton recognition. These preliminary findings further suggest that near-distance service interaction may benefit from both distance-aware robot policy and robust wide-field smartphone-based AR/body-tracking support that can preserve skeleton completeness and metric spatial stability. Further evaluation is needed to assess generalizability beyond this setting.