Intelligent Space — concept and contents

Lee, Joo‐Ho; Hashimoto, Hideki

doi:10.1163/156855302760121936

Cited by 318 publications

(141 citation statements)

References 5 publications

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“…where y k is an element of the output vector y, that is, the floor number that is expressed by 1-of-K coding scheme and the position on the floor estimated by the system; x i is an element of the input vector x, that is, the signal strength; and w (2) kj and w (1) ji are weight parameters of the neural network. N is the number of the floors of the building.…”

Section: Estimation Of Position and Floor Number Using Neural Networkmentioning

confidence: 99%

“…We set the tuning parameter = 0.1. The initial values of weight parameters of the network w (2) kj and w (1) ji were set at random with a uniform distribution. The range of initial values of the weight parameters is to the interval [−0.3, 0.3].…”

Section: Experimental Settingmentioning

confidence: 99%

“…By integrating the data and stored information on objects and places, people and robots can be provided with information on the location of the devices [1]. Many kinds of localization services have been presented for personal devices and mobile robots [2,3]; however, the localization method takes much cost and needs special devices mostly.…”

Section: Introductionmentioning

confidence: 99%

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Localization of Mobile Client in Multistory Building using Public Wireless LAN System

Umetani

Yamashita

Tamura

2011

Stochastic Systems Theory and its Applications (SSS)

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This paper describes a method for the localization of wireless mobile clients in multistory buildings using a public wireless LAN system. The global positioning system (GPS) is used for the outdoor localization of a mobile client carried by humans or mobile robots; however, it is difficult to estimate the global position of the mobile client in multistory buildings since the GPS is not suitable for indoor localization. The proposed method uses public wireless LAN access points which are settled three-dimensionally in a building. The application of the method involves the assumption that the humans or robots carrying the mobile client move horizontally on each floor in the building. The method simultaneously estimates the position of the mobile client and its floor number. Experimental results indicate that the proposed method is feasible.

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Section: Estimation Of Position and Floor Number Using Neural Networkmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%

See 1 more Smart Citation

Localization of Mobile Client in Multistory Building using Public Wireless LAN System

Umetani

Yamashita

Tamura

2011

Stochastic Systems Theory and its Applications (SSS)

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“…The integration of observed data and stored information regarding physical objects and places, especially the location of a mobile client, is a crucial issue for workers and robots who need this information for various applications [1]. A localization method that uses only a few devices is desirable for practical applications such as mobile robot navigation or information services for users with mobile phones.…”

Section: Introductionmentioning

confidence: 99%

State Estimation of Wireless Mobile Client based on Sensor Data Fusion for Localization in Multistory Building

Umetani

Yamane

Yamashita

et al. 2013

Transactions of ISCIE

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This paper describes a state space model for estimating the location of a mobile wireless client based on sensor data fusion. Data pertaining to the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. We use the strength of signals received by wireless LAN access points to locate mobile clients in multistory buildings. We also use the acceleration of the mobile client as sensor data. The output of an accelerometer is prone to uncertainty; we use a probabilistic model of the location estimated by wireless LAN signals and the acceleration acquired by a sensor to create an observation model. In addition, a simplified measurement method is proposed for use by a fingerprinting-type estimation system based on human footsteps. Experimental results show the feasibility of the proposed method.

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“…Systems consisting of many heterogeneous robots which are pervasively distributed in the environment, and which cooperate to perform possibly complex tasks, are becoming increasingly popular in the field of autonomous robotics. Systems of this type are being studied under different names, including network robot systems [1], intelligent spaces [2], sensor-actuator networks [3], ubiquitous robotics [4], and Peis-Ecology [5]. Common to these systems is the fact that the term "robot" is taken in a wide sense, including both mobile robots, fixed sensors or actuators, and automated home appliances.…”

Section: Introductionmentioning

confidence: 99%

Autonomous functional configuration of a network robot system

Lundh

Karlsson

Saffiotti

2008

Robotics and Autonomous Systems

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We consider distributed systems of networked robots in which: (1) each robot includes sensing, acting and/or processing modular functionalities; and (2) robots can help each other by offering those functionalities. A functional configuration is any way to allocate and connect functionalities among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In this paper, we propose an approach to automatically generate, at run time, a functional configuration of a network robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. Moreover, our configuration planner can be combined with an action planner to deal with tasks that require sequences of configurations. We illustrate our approach on a specific type of network robot system, called Peis-Ecology, and show experiments in which a sequence of configurations is automatically generated and executed on real robots. These experiments demonstrate that our self-configuration approach can help the system to achieve greater autonomy, flexibility and robustness.

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Intelligent Space — concept and contents

Cited by 318 publications

References 5 publications

Localization of Mobile Client in Multistory Building using Public Wireless LAN System

Localization of Mobile Client in Multistory Building using Public Wireless LAN System

State Estimation of Wireless Mobile Client based on Sensor Data Fusion for Localization in Multistory Building

Autonomous functional configuration of a network robot system

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