A computational model of human-robot spatial interactions based on a qualitative trajectory calculus

In this paper we propose a probabilistic sequential model of Human-Robot Spatial Interaction (HRSI) using a well-established Qualitative Trajectory Calculus (QTC) to encode HRSI between a human and a mobile robot in a meaningful, tractable, and systematic manner. Our key contribution is to utilise QTC as a state descriptor and model HRSI as a probabilistic sequence of such states. Apart from the sole direction of movements of human and robot modelled by QTC, attributes of HRSI like proxemics and velocity profiles play vital roles for the modelling and generation of HRSI behaviour. In this paper, we particularly present how the concept of proxemics can be embedded in QTC to facilitate richer models. To facilitate reasoning on HRSI with qualitative representations, we show how we can combine the representational power of QTC with the concept of proxemics in a concise framework, enriching our probabilistic representation by implicitly modelling distances. We show the appropriateness of our sequential model of QTC by encoding different HRSI behaviours observed in two spatial interaction experiments. We classify these encounters, creating a comparative measurement, showing the representational capabilities of the model

Robot deployment in long-term care: a case study of a mobile robot in physical therapy

Background. Healthcare systems in industrialised countries are challenged to provide care for a growing number of older adults. Information technology holds the promise of facilitating this process by providing support for care staff, and improving wellbeing of older adults through a variety of support systems. Goal. Little is known about the challenges that arise from the deployment of technology in care settings; yet, the integration of technology into care is one of the core determinants of successful support. In this paper, we discuss challenges and opportunities associated with technology integration in care using the example of a mobile robot to support physical therapy among older adults with cognitive impairment in the European project STRANDS. Results and discussion. We report on technical challenges along with perspectives of physical therapists, and provide an overview of lessons learned which we hope will help inform the work of researchers and practitioners wishing to integrate robotic aids in the caregiving process

Topological Mapping and Navigation in Real-World Environments

We introduce the Hierarchical Hybrid Spatial Semantic Hierarchy (H2SSH), a hybrid topological-metric map representation. The H2SSH provides a more scalable representation of both small and large structures in the world than existing topological map representations, providing natural descriptions of a hallway lined with offices as well as a cluster of buildings on a college campus. By considering the affordances in the environment, we identify a division of space into three distinct classes: path segments afford travel between places at their ends, decision points present a choice amongst incident path segments, and destinations typically exist at the start and end of routes. Constructing an H2SSH map of the environment requires understanding both its local and global structure. We present a place detection and classification algorithm to create a semantic map representation that parses the free space in the local environment into a set of discrete areas representing features like corridors, intersections, and offices. Using these areas, we introduce a new probabilistic topological simultaneous localization and mapping algorithm based on lazy evaluation to estimate a probability distribution over possible topological maps of the global environment. After construction, an H2SSH map provides the necessary representations for navigation through large-scale environments. The local semantic map provides a high-fidelity metric map suitable for motion planning in dynamic environments, while the global topological map is a graph-like map that allows for route planning using simple graph search algorithms. For navigation, we have integrated the H2SSH with Model Predictive Equilibrium Point Control (MPEPC) to provide safe and efficient motion planning for our robotic wheelchair, Vulcan. However, navigation in human environments entails more than safety and efficiency, as human behavior is further influenced by complex cultural and social norms. We show how social norms for moving along corridors and through intersections can be learned by observing how pedestrians around the robot behave. We then integrate these learned norms with MPEPC to create a socially-aware navigation algorithm, SA-MPEPC. Through real-world experiments, we show how SA-MPEPC improves not only Vulcan’s adherence to social norms, but the adherence of pedestrians interacting with Vulcan as well.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144014/1/collinej_1.pd

A Computational Model of Human-Robot Spatial Interactions Based on a Qualitative Trajectory Calculus

In this paper we propose a probabilistic sequential model of Human-Robot Spatial Interaction (HRSI) using a well-established Qualitative Trajectory Calculus (QTC) to encode HRSI between a human and a mobile robot in a meaningful, tractable, and systematic manner. Our key contribution is to utilise QTC as a state descriptor and model HRSI as a probabilistic sequence of such states. Apart from the sole direction of movements of human and robot modelled by QTC, attributes of HRSI like proxemics and velocity profiles play vital roles for the modelling and generation of HRSI behaviour. In this paper, we particularly present how the concept of proxemics can be embedded in QTC to facilitate richer models. To facilitate reasoning on HRSI with qualitative representations, we show how we can combine the representational power of QTC with the concept of proxemics in a concise framework, enriching our probabilistic representation by implicitly modelling distances. We show the appropriateness of our sequential model of QTC by encoding different HRSI behaviours observed in two spatial interaction experiments. We classify these encounters, creating a comparative measurement, showing the representational capabilities of the model

Pflegeroboter

Dieses Open-Access-Buch bündelt technische, wirtschaftliche, medizinische und ethische Reflexionen über Pflegeroboter. Pflegeroboter, im Moment noch mehrheitlich Prototypen, unterstützen oder ersetzen menschliche Pflegekräfte bzw. Betreuer. Sie bringen Kranken und Alten die benötigten Medikamente und Nahrungsmittel, helfen beim Hinlegen und Aufrichten oder alarmieren den Notdienst. Vorteile von Pflegerobotern sind durchgehende Verwendbarkeit und gleichbleibende Qualität der Dienstleistung. Nachteile sind Kostenintensität (bei möglicher Amortisation) und Komplexität der Anforderungen. Unter der wissenschaftlichen Leitung von Prof. Dr. Oliver Bendel trafen sich im September 2017 Vertreter verschiedener wissenschaftlicher Disziplinen im Rahmen eines Ladenburger Diskurses der Daimler und Benz Stiftung, um über den aktuellen und künftigen Einsatz von Pflegerobotern zu sprechen und Forschungspotenziale zu identifizieren. Die Autoren gehen in ihren Beiträgen auch Fragen aus Wirtschafts-, Medizin- und Informationsethik nach: Wer trägt die Verantwortung bei einer fehlerhaften Betreuung und Versorgung durch die Maschine? Inwieweit kann diese die persönliche und informationelle Autonomie des Patienten unterstützen oder gefährden? Ist der Roboter eine Entlastung oder ein Konkurrent für Pflegekräfte? Antworten müssen von Wissenschaft und Gesellschaft gefunden werden