Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test
In hospitals, trained medical staff are often, in addition to performing complex procedures, spending valuable time on secondary tasks such as transporting samples and medical equipment; or even guiding patients and visitors around the premises. If these non-medical tasks were automated by deploying...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2022.922835/full |
| _version_ | 1811283498666819584 |
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| author | Robert Valner Houman Masnavi Igor Rybalskii Rauno Põlluäär Erik Kõiv Alvo Aabloo Karl Kruusamäe Arun Kumar Singh |
| author_facet | Robert Valner Houman Masnavi Igor Rybalskii Rauno Põlluäär Erik Kõiv Alvo Aabloo Karl Kruusamäe Arun Kumar Singh |
| author_sort | Robert Valner |
| collection | DOAJ |
| description | In hospitals, trained medical staff are often, in addition to performing complex procedures, spending valuable time on secondary tasks such as transporting samples and medical equipment; or even guiding patients and visitors around the premises. If these non-medical tasks were automated by deploying mobile service robots, more time can be focused on treating patients or allowing well-deserved rest for the potentially overworked healthcare professionals. Automating such tasks requires a human-aware robotic mobility system that can among other things navigate the hallways of the hospital; predictively avoid collisions with humans and other dynamic obstacles; coordinate task distribution and area coverage within a fleet of robots and other IoT devices; and interact with the staff, patients and visitors in an intuitive way. This work presents the results, lessons-learned and the source code of deploying a heterogeneous mobile robot fleet at the Tartu University Hospital, performing object transportation tasks in areas of intense crowd movement and narrow hallways. The primary use-case is defined as transporting time-critical samples from an intensive care unit to the hospital lab. Our work builds upon Robotics Middleware Framework (RMF), an open source, actively growing and highly capable fleet management platform which is yet to reach full maturity. Thus this paper demonstrates and validates the real-world deployment of RMF in an hospital setting and describes the integration efforts. |
| first_indexed | 2024-04-13T02:13:44Z |
| format | Article |
| id | doaj.art-81bc53e2fbbd4ea79d42ac08a609be98 |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-04-13T02:13:44Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj.art-81bc53e2fbbd4ea79d42ac08a609be982022-12-22T03:07:13ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442022-08-01910.3389/frobt.2022.922835922835Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field testRobert ValnerHouman MasnaviIgor RybalskiiRauno PõlluäärErik KõivAlvo AablooKarl KruusamäeArun Kumar SinghIn hospitals, trained medical staff are often, in addition to performing complex procedures, spending valuable time on secondary tasks such as transporting samples and medical equipment; or even guiding patients and visitors around the premises. If these non-medical tasks were automated by deploying mobile service robots, more time can be focused on treating patients or allowing well-deserved rest for the potentially overworked healthcare professionals. Automating such tasks requires a human-aware robotic mobility system that can among other things navigate the hallways of the hospital; predictively avoid collisions with humans and other dynamic obstacles; coordinate task distribution and area coverage within a fleet of robots and other IoT devices; and interact with the staff, patients and visitors in an intuitive way. This work presents the results, lessons-learned and the source code of deploying a heterogeneous mobile robot fleet at the Tartu University Hospital, performing object transportation tasks in areas of intense crowd movement and narrow hallways. The primary use-case is defined as transporting time-critical samples from an intensive care unit to the hospital lab. Our work builds upon Robotics Middleware Framework (RMF), an open source, actively growing and highly capable fleet management platform which is yet to reach full maturity. Thus this paper demonstrates and validates the real-world deployment of RMF in an hospital setting and describes the integration efforts.https://www.frontiersin.org/articles/10.3389/frobt.2022.922835/fullrobot fleetROShealthcarehuman-robot interactionmobile roboticsobject transportation |
| spellingShingle | Robert Valner Houman Masnavi Igor Rybalskii Rauno Põlluäär Erik Kõiv Alvo Aabloo Karl Kruusamäe Arun Kumar Singh Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test Frontiers in Robotics and AI robot fleet ROS healthcare human-robot interaction mobile robotics object transportation |
| title | Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test |
| title_full | Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test |
| title_fullStr | Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test |
| title_full_unstemmed | Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test |
| title_short | Scalable and heterogenous mobile robot fleet-based task automation in crowded hospital environments—a field test |
| title_sort | scalable and heterogenous mobile robot fleet based task automation in crowded hospital environments a field test |
| topic | robot fleet ROS healthcare human-robot interaction mobile robotics object transportation |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2022.922835/full |
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