CHARMIE: a collaborative healthcare and home service and assistant robot for elderly care

The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The author T.R. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH). The author F.G. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/145993/2019], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH)

Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices

Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.Comment: Accepted to The 11th International Conference on Machine Vision (ICMV 2018). Project site: https://zhengyiluo.github.io/projects/cloudchaser

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.This work has been partially funded by the Basque Government, Spain, grant number IT900-16, and the Spanish Ministry of Economy and Competitiveness (MINECO), grant number RTI2018-093337-B-I00