Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots

Suzuki, Einoshin; Deguchi, Yutaka; Takayama, Daisuke; Takano, Shigeru; Scuturici, Vasile Marian; Petit, Jean-Marc

doi:10.1007/978-3-319-08732-0_5

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…The "AssistiveAble" project has shown progress in the development of an assistive robot for a social assisted house environment [31]. This project focuses on older adults with mild cognitive impairment who live alone.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

et al. 2019

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This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.

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Section: Related Workmentioning

confidence: 99%

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

et al. 2019

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“…For instance, a monitoring robot would greatly benefit from the ability to analyze its observations and discover patterns to adapt to its target person. With this motivation in our mind, we proposed several autonomous mobile robots which perform online clustering to discover useful patterns [13,14,15]. However, to construct such an application system with these mobile robots, we have to overcome the obstacles of developing, managing, and controlling various components.…”

Section: Introductionmentioning

confidence: 99%

“…Existing works in developing systems of autonomous robots can be classified into a declarative language based approach [4,8] and a machine learning based approach [1,10], which we have discussed in more detail in [13]. A work in the former category typically relies on a domain specific language with an emphasis on either the service aspects [4] or the ontology aspects [8].…”

Section: Introductionmentioning

confidence: 99%

“…We have been working on integrating the declarative language based approach and the machine learning based approach using a service oriented DBMS (Database Management System) [13]. In this approach, all components such as mobile robots, sensors, and actuators are handled as services in a uniform way, which enables us an agile development and efficient management of the system.…”

Section: Introductionmentioning

confidence: 99%

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Multiple-robot monitoring system based on a service-oriented DBMS

Deguchi

Takayama

Takano

et al. 2014

Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

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In this paper, we present a human-targeted monitoring system composed of two autonomous mobile robots based on a service-oriented DBMS, mainly from the viewpoint of positioning control. Each robot is equipped with a Kinect and monitors the target human from appropriate angles and distances. The service-oriented DBMS, which manages the monitoring system and enables a rapid development and extension of the system, views each robot as a data source which generates a data stream to be stored and processed in the DBMS. The results of the experiments conducted in a real office are promising.

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“…Initially a Labelled Non-Stationary Environment (ILNSE) addresses both EVL and NSEs issues simultaneously, for example, autonomous robots [67] are initially trained inside a specific environment on labelled data and known classes. Later, they are sent to explore an unknown environment without the supervision of humans.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments

2022

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Towards Facilitating the Development of Monitoring Systems with Low-Cost Autonomous Mobile Robots

Cited by 7 publications

References 22 publications

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

Multiple-robot monitoring system based on a service-oriented DBMS

Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments

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