Controlling a service robot in a smart home with behavior planning and learning

Chen, Chia-Hung; Liu, Alan; Zhou, Pei-Chuan

doi:10.1109/smc.2014.6974356

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Preferences. A user may select and customise recipes [Kotsovinos and Vukovic 2005], indicate personalised choices on various devices [Ding et al 2006;Fraile et al 2013], and provide preferences on services [Liang et al 2010;Song and Lee 2013;Chen et al 2014], daily activities [Mastrogiovanni et al 2010;Vaquero et al 2015;Köckemann et al 2014], domains Yau and Buduru 2014], treatments [S ánchez-Garzón et al 2012], timetables ], companion systems [Honold et al 2014], and products ]. In Ranganathan and Campbell [2004], user preferences are in the form of utility u for each predicate in different contexts, where u ∈ [−10, 10].…”

Section: Review Of Primary Studiesmentioning

confidence: 99%

Automated Planning for Ubiquitous Computing

Georgievski

Aiello

2016

ACM Comput. Surv.

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The goal of ubiquitous computing is to create ambience in which one’s experiences and quality of life are improved by monitoring and assisting people using ubiquitous technologies and computation in coherence. The continuous advancements of involved technologies, such as wireless communications, mobile devices, and sensors, imply fast evolution of ubiquitous computing environments too. The complexity of these environments is reaching a point where traditional solutions simply no longer work. The environments are in need of computational techniques that can deal with the evolution and uncertainty of ubiquitous computing environments dynamically and automatically. Artificial Intelligence (AI) can contribute towards satisfying this future scenario in many ways, while numerous approaches inspired by work in the AI planning community have already been designed for ubiquitous computing. We devote this study to investigate the current progress of AI planning for ubiquitous computing by analysing those approaches. We rigorously search for and select relevant literature out of which we extract qualitative information. Using the extracted qualities, we derive a generic framework that consists of aspects important to planning for ubiquitous computing. The framework’s main purpose is to facilitate the understanding of those aspects, and classify the literature according to them. We then analyse the literature in a consolidated way, and identify future challenges of planning for ubiquitous computing.

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Section: Review Of Primary Studiesmentioning

confidence: 99%

Automated Planning for Ubiquitous Computing

Georgievski

Aiello

2016

ACM Comput. Surv.

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“…The ENRICHME project [12] is an example concept that combines robotics and other intelligent systems in order to supervise and improve the quality of life of fragile elderly people. The paper [13] presented a smart home which monitors and analyzes the environmental conditions to generate and deliver sets of sequences that are used to control a service robot operating inside the home. The use of service robots for performing healthcare routines is also often proposed in the scientific literature.…”

Section: Introductionmentioning

confidence: 99%

Automatic Supervision of Temperature, Humidity, and Luminance with an Assistant Personal Robot

et al. 2017

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Smart environments and Ambient Intelligence (AmI) technologies are defining the future society where energy optimization and intelligent management are essential for a sustainable advance. Mobile robotics is also making an important contribution to this advance with the integration of sensors and intelligent processing algorithms. This paper presents the application of an Assistant Personal Robot (APR) as an autonomous agent for temperature, humidity, and luminance supervision in human-frequented areas. The robot multiagent capabilities allow gathering sensor information while exploring or performing specific tasks and then verifying human comfortability levels. The proposed methodology creates information maps with the distribution of temperature, humidity, and luminance and interprets such information in terms of comfort and warns about corrective actuations if required.

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