Present state and future of Intelligent Space—Discussion on the implementation of RT in our environment—

Hashimoto, Hideki

doi:10.1007/s10015-006-0416-6

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Step4:Usingx k|k and P (1) k|k , compute the one-step prediction vision value by Kalman filterand the corresponding covariance matrix S (2) k and Kalman gain K (2) k ;…”

Section: Multi-sensor Probability Data Association Object Trackingmentioning

confidence: 99%

“…Suppose sensor i has m (i) k measurements in the tracking gate, which is represented by z (i)(j) k j = 1, 2, m (j) k then the object tracking algorithm based on multi-sensor data association [15] can be illustrated as following: Step1:Usingx k−1|k−1 and covariance matrix P k−1|k−1 according to formula (6), compute the predicted value: which includes statex k|k−1 , covariance matrix P k|k−1 measurementsẑ (1) k|k−1 and its corresponding difference covariance matrix, S k (1),Kalman gain, K k (1);…”

Section: Multi-sensor Probability Data Association Object Trackingmentioning

confidence: 99%

“…And the space can collect the environmental information through different sensors so as to understand the state of the space. The combination of the space and the robot is a frontier direction [1,2], and it supplies for the robot a good space to complete the task and understand the environment. And the object location and tracking is the key and the fundamental problem for the intelligent space to understand the environment.…”

Section: Introductionmentioning

confidence: 99%

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Object localization and tracking based on multiple sensor fusion in intelligent home

Yin

Tian

2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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A novel scheme for object localization and tracking under family environment is presented based on fusion of multiple sensors, which include two laser sensors and camera sensors. The two laser sensors and two cameras are used to locate the object separately, and multiple sensors probability data association fusion algorithm is used to track the objects. Firstly, object detection is realized by laser sensors and vision sensors separately. Secondly, the laser data is fused by Extended Kalman Filter. To obtain the vision location results, background model is built by adaptive background updating based on motion history images. Background subtraction is used to acquire the original location result, which is filtered by Kalman Filter. Finally, multiple sensors probability data association fusion algorithm is used to fuse the different kinds of data. Experimental results show that the scheme can efficiently solve the problem of object localization and tracking.

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“…Step4:Usingx k|k and P (1) k|k , compute the one-step prediction vision value by Kalman filterand the corresponding covariance matrix S (2) k and Kalman gain K (2) k ;…”

Section: Multi-sensor Probability Data Association Object Trackingmentioning

confidence: 99%

Section: Multi-sensor Probability Data Association Object Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Object localization and tracking based on multiple sensor fusion in intelligent home

Yin

Tian

2014

The 26th Chinese Control and Decision Conference (2014 CCDC)

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“…In the context of robotics cooperation and tele-operation, it is of foremost importance to minimize the computational load of the on-board electronics system [ 15 , 25 , 26 ], as well as to reduce the communications burden. In a previous work [ 27 ], a conservative design dealing with the maximum delay was evaluated and implemented.…”

Section: Introductionmentioning

confidence: 99%

Aperiodic Linear Networked Control Considering Variable Channel Delays: Application to Robots Coordination

Santos

Espinosa

Santiso

et al. 2015

Sensors

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One of the main challenges in wireless cyber-physical systems is to reduce the load of the communication channel while preserving the control performance. In this way, communication resources are liberated for other applications sharing the channel bandwidth. The main contribution of this work is the design of a remote control solution based on an aperiodic and adaptive triggering mechanism considering the current network delay of multiple robotics units. Working with the actual network delay instead of the maximum one leads to abandoning this conservative assumption, since the triggering condition is fixed depending on the current state of the network. This way, the controller manages the usage of the wireless channel in order to reduce the channel delay and to improve the availability of the communication resources. The communication standard under study is the widespread IEEE 802.11g, whose channel delay is clearly uncertain. First, the adaptive self-triggered control is validated through the TrueTime simulation tool configured for the mentioned WiFi standard. Implementation results applying the aperiodic linear control laws on four P3-DX robots are also included. Both of them demonstrate the advantage of this solution in terms of network accessing and control performance with respect to periodic and non-adaptive self-triggered alternatives.

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“…However, in the context of robotics formation or applications based on robotics tele-operation is of foremost importance to minimize the computational load of the on-board electronics system of the mobile unit [9], [14], as well as to reduce the communications burden. This justifies the objective of this paper: the design and implementation of NCS for robotic applications minimizing the wireless communications load enabling other applications to share the channel bandwidth and the on-board electronic resources.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Self-triggered Control for Remote Operation of Wifi Linked Robots

Santos

Mazo

Santiso

et al. 2014

ROBOT2013: First Iberian Robotics Conference

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Abstract. We consider the problem of remotely operating an autonomous robot through a wireless communication channel. Our goal is to achieve a satisfactory tracking performance while reducing network usage. To attain this objective we implement a self-triggered strategy that adjusts the triggering condition to the observed tracking error. After the theoretical justification we present experimental results from the application of this adaptive self-triggered approach on a P3-DX mobile robot remotely controlled. The experiments show a relevant reduction on the generated network traffic compared to a periodic implementation and to a non-adaptive self-triggered approach, while the tracking performance is barely degraded.

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Present state and future of Intelligent Space—Discussion on the implementation of RT in our environment—

Cited by 10 publications

References 10 publications

Object localization and tracking based on multiple sensor fusion in intelligent home

Object localization and tracking based on multiple sensor fusion in intelligent home

Aperiodic Linear Networked Control Considering Variable Channel Delays: Application to Robots Coordination

Adaptive Self-triggered Control for Remote Operation of Wifi Linked Robots

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