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Group abstraction for assisted navigation of social activities in intelligent environments

Group abstraction for assisted navigation of social activities in intelligent environments The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO’s robotic assistants must, therefore, be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using statistical model checking. In this work, we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zürich pedestrian motion dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Reliable Intelligent Environments Springer Journals

Group abstraction for assisted navigation of social activities in intelligent environments

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References (48)

Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer International Publishing AG, part of Springer Nature
Subject
Computer Science; Performance and Reliability; Software Engineering/Programming and Operating Systems; Artificial Intelligence (incl. Robotics); Simulation and Modeling; User Interfaces and Human Computer Interaction; Health Informatics
ISSN
2199-4668
eISSN
2199-4676
DOI
10.1007/s40860-018-0058-1
Publisher site
See Article on Publisher Site

Abstract

The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO’s robotic assistants must, therefore, be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using statistical model checking. In this work, we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zürich pedestrian motion dataset.

Journal

Journal of Reliable Intelligent EnvironmentsSpringer Journals

Published: May 25, 2018

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