Robotic UBIquitous COgnitive Network

Amato, Giuseppe; Broxvall, Mathias; Chessa, Stefano; Dragone, Mauro; Gennaro, Claudio; López, Rafael Lemuz; Maguire, Liam; McGinnity, T.M.; Micheli, Alessio; Rentería, Arantxa; O’Hare, Gregory M. P.; Pecora, Federico

doi:10.1007/978-3-642-28783-1_23

Cited by 18 publications

(4 citation statements)

References 3 publications

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“…A further application of RC models for real-time user localization in indoor en-vironments is presented in [67], in which by adopting a hybrid approach, RC networks have been showed to provide significant benefits to the accuracy of RSSbased localization systems. It is also worth mentioning the recent results of the European FP7 Project RU-BICON [23,24], whose goal was to design and develop a self-learning robotic ecology made up of sensors, actuators and robotic devices. Within the aims of the RUBICON project, ESNs distributed on the lowpowerful nodes of a Wireless Sensor Network (WSN) have been used to approach supervised computational tasks in the field of AAL, and pertaining to the classification of human activities, using input data streams coming from sensorized environments.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, RC models have proved to be particularly suitable for processing noisy information streams originated by networks of heterogeneous sensors, resulting in successful real-world applications in tasks related to AAL and human activity recognition (see e.g. [18,19,20,21,22]), and allowing to successfully build intelligent sensor networks tailored to specific activity recognition contexts, as testified by the recent successful results of the RUBICON Project [23,24] and detailed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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Human activity recognition using multisensor data fusion based on Reservoir Computing

Palumbo

Gallicchio

Pucci

et al. 2016

AIS

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139

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Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Human activity recognition using multisensor data fusion based on Reservoir Computing

Palumbo

Gallicchio

Pucci

et al. 2016

AIS

Self Cite

139

View full text Add to dashboard Cite

show abstract

“…A key challenge in this type of solutions is how to make them self-adaptive, so that they can be more easily applied to real-world settings without requiring costly human supervision and configuration every time they need to be tailored to different environments and configured to suit the needs of their users. This is the focus of the EU FP7 project RUBICON, (Robotic UBIquitous COgnitive Network) [7,24,4], which has built robotic ecologies consisting of mobile robotic devices, sensors, effectors, and appliances cooperating to perform complex tasks such as supporting elders to live independently. While the emphasis in RUBICON is providing robotic ecologies with self-adaptation features by equipping them with cognitive abilities, such as learning and planning, this paper focuses on the underlying communication capabilities required by this type of solutions.…”

Section: Introductionmentioning

confidence: 99%

A Communication Layer for Integrated Sensors and Robotic ecology Solutions to Ambient Intelligence

Amato,

Chessa,

Dragone

et al. 2021

Preprint

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This paper presents a communication framework built to simplify the construction of robotic ecologies, i.e., networks of heterogeneous computational nodes interfaced with sensors, actuators, and mobile robots. Building integrated ambient intelligence (AmI) solutions out of such a wide range of heterogeneous devices is a key requirement for a range of application domains, such as home automation, logistic, security and Ambient Assisted Living (AAL). This goal is challenging since these ecologies need to adapt to changing environments and especially when they include tiny embedded devices with limited computational resources. We discuss a number of requirements characterizing this type of systems and illustrate how they have been addressed in the design of the new communication framework. The most distinguishing aspect of our frameworks is the transparency with which the same communication features are offered across heterogeneous programming languages and operating systems under a consistent API. Finally, we illustrate how the framework has been used to bind together and to support the operations of all the components of adaptive robotic ecologies in two real-world test-beds.

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“…Another important issue of ubiquitous robotic systems is the development of a task level learning and planning module that handles various tasks and dynamic environment without recoding the robots [ 7 , 8 ]. This is also critical for smart factories, where there may be a variety of orders and different situations for each order.…”

Section: Introductionmentioning

confidence: 99%

Ubiquitous Robotic Technology for Smart Manufacturing System

Wang

Zhu

Wang

et al. 2016

Computational Intelligence and Neuroscience

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As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods.

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Robotic UBIquitous COgnitive Network

Cited by 18 publications

References 3 publications

Human activity recognition using multisensor data fusion based on Reservoir Computing

Human activity recognition using multisensor data fusion based on Reservoir Computing

A Communication Layer for Integrated Sensors and Robotic ecology Solutions to Ambient Intelligence

Ubiquitous Robotic Technology for Smart Manufacturing System

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