A multihypothesis set approach for mobile robot localization using heterogeneous measurements provided by the Internet of Things

Hospitals are rightfully considered a field of indoor logistic robotics of high commercial potential. However, today, only a handful of mobile robotic solutions for hospital logistics exist that have failed to trigger widespread acceptance by the market. This is because existing systems require costly infrastructure installation, they do not easily integrate to corporate IT solutions, are not adequately shielded from cybersecurity threats, and as a result, they do not fully automate procedures and traceability of the items they carry. Moreover, existing systems are limited on scope, focusing only on delivery services, and hence do not provide any other type of support to the medical and nursing staff. ENDORSE system will address the aforementioned technical challenges and functional limitations by pursuing four innovation pillars: (i) infrastructure-less multirobot indoor navigation; (ii) advanced Human-Robot Interaction (HRI) for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; (iii) deployment of the ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions, complying with GDPR data security requirements; (iv) reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring on a fleet of mobile robots, facilitating connectivity to cloud-based Electronic Health Records (EHR), and validated in an operational hospital environment for realistic assessment.

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Section: B Internet-based Indoors Localization Technologiesmentioning

confidence: 99%

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

Ramdani¹,

Panayides²,

Karamousadakis³

et al. 2019

2019 20th IEEE International Conference on Mobile Data Management (MDM)

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“…These techniques can operate efficiently with sparse, asynchronous and heterogeneous measurements while being robust to the presence of non-consistent measurements, inaccuracy in environment modelling, and drift and inaccuracy in the robot evolution model. Furthermore, they can naturally track multi-hypotheses about robot pose, hence offer a solution technique for the robot global localization problem (Amri, Becis, Aubry, & Ramdani, 2015) (Colle & Galerne, 2017). In ENDORSE project, a comparison and a selection of onboard sensors, that are employed for localization, will take place, in order to improve accuracy and stability of global localization (localization techniques fail, become less accurate in large spaces where Lidar or RGB-D have long travel distances) and reduce the computational burden of SLAM (which might result in deployment of less expensive hardware).…”

Section: Internet-based Indoors Localization Technologiesmentioning

confidence: 99%

ENDORSE Concept

Vartholomaios

Ramdani

Christophorou

et al. 2019

International Journal of Reliable and Quality E-Healthcare

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Hospitals are considered a field of logistic robotics of high commercial potential and therefore a handful of mobile robot solutions exist. However, they have failed to trigger widespread acceptance by the market. The ENDORSE system will pursue 4 innovation pillars: an infrastructure-less multi-robot navigation, i.e. minimum installation of sensors and communications buses inside the building for the localization of robots, targets and docking stations; advanced HRI for resolving deadlocks and achieving efficient sharing of space resources in crowded environments; the deployment of ENDORSE software as a cloud-based service facilitating its integration with corporate software solutions such as ERP and CRM, complying with GDPR data security requirements; and allowing for reconfigurable and modular hardware architectures so that diverse modules can be easily swapped. ENDORSE functionality will be demonstrated via the integration of an e-diagnostic support module for vital signs monitoring, facilitating connectivity to cloud-based EHR, and validated in an operational hospital environment for realistic assessment.

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“…The representative algorithms of the continuous approach include the Kalman filter , extended Kalman filter , and unscented Kalman filter . Because these methods maintain only a single state, they are mainly used to track the position of the robot after the global localization .…”

Section: Related Workmentioning

confidence: 99%

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

Lee

Myung

2019

ETRI Journal

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This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

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A multihypothesis set approach for mobile robot localization using heterogeneous measurements provided by the Internet of Things

Cited by 13 publications

References 24 publications

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

A Safe, Efficient and Integrated Indoor Robotic Fleet for Logistic Applications in Healthcare and Commercial Spaces: The ENDORSE Concept

ENDORSE Concept

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

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