Tracking Mobile Robot in Indoor Wireless Sensor Networks

Tracking a mobile node using wireless sensor network (WSN) under cooperative system among anchor node and the mobile node, has been discussed in this work, interested to the indoor positioning applications. Developing an indoor location tracking system based on received signal strength indicator (RSSI) of WSN is considered cost effective and the simplest method. The suitable technique for estimating position out of RSSI measurements is the extended Kalman filter (EKF) which is especially used for nonlinear data as RSSI. In order to reduce the estimated errors from EKF algorithm, this work adopted forward data processing of the EKF algorithm to improve the accuracy of the filtering output, its called iterated extended Kalman filter (IEKF). However, using IEKF algorithm should know the stopping criterion value that is influenced to the maximum number iterations of this system. The number of iterations performed will be affected to the computation time although it can improve the estimation position. In this paper, we propose modified IEKF for mobile cooperative tracking system within only 4 iterations number. The illustrated results using RSSI measurements and simulation in MATLAB show that our proposed method has the capability to reduce error estimation percentage up to 19.3% , with MSE (mean square error) 0.88 m compared with a conventional IEKF algorithm with MSE 1.09 m. The time computation performance of our proposed method achieved in 3.55 seconds which is better than adding more iteration process.

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“…As shown in Fig. 2, there are 3 phases of EKF algorithm: initialization, prediction and update [1]- [4], [7], [9], [13]- [15], [17]- [21].…”

Section: Extended Kalman Filter (Ekf)mentioning

confidence: 99%

“…d est (1,2,3) is the distance estimation of the mobile node to the anchor node that was obtained from PLE value eq. (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). The covariance matrix is obtained from the coordinate estimation of trilateration method that is described in this following matrix:…”

Section: E Proposed Modified Iekf Algorithmmentioning

confidence: 99%

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Ainul

Kristalina

Sudarsono

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…The communication subsystem of the robot features traditional cabled Ethernet connections or redundant wireless systems. Redundancy is ensured by the mutual combination of WiFi signals in the 2.4 GHz and 5 GHz bands [5], [6].…”

Section: Description Of the Mobile Robotic Systemmentioning

confidence: 99%

Collision-free manipulation of a robotic arm using the MS Windows Kinect 3D optical system

Vachálek

Capucha

Krasnansky

et al. 2015

2015 20th International Conference on Process Control (PC)

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This article presents the collision-free manipulation of a robotic arm mounted to a mobile robotic unit, using the MS Windows Kinect 3D optical camera system. The 3D optical system is used to recognize objects, aiding the collision-free manipulation of the said objects with the arm of a mobile robotic system (MRS). The placement of the optical system directly on the robotic arm is essential to ensure the autonomy of the overall system. The MRS is able to recognize pre-defined objects in three dimensional space and approach and manipulate these automatically using its robotic arm. The aim of this article is to present the algorithms used in the mobile robot to guarantee the collision-free manipulation of the MRS with objects. The novelty of our approach lies in the a priori collision avoidance strategy, instead of solving collision states as they occur. In addition to this, the article presents the localization of objects in space. For this, randomly placed balls are localized by the MRS, estimating their real coordinates. Instead of using the standard RGB colorspace, we propose to utilize the HSV colorspace to assess object coordinates, since it provides more consistent results in case the ambient lightning intensity varies.

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“…Location information is essential for the robot to complete the tasks. In many mobile robot applications, especially in the industry, high localization and positioning accuracy are required (Zhang et al, 2014;Röwekämper et al, 2012). Domestic robots and automated guided vehicles (AGV) are examples of mobile robots used inside homes and in the industry, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…This process can be seen as a sequentially performed localization of a moving robot. Wireless sensor networks (WSNs) are usually used to perform tracking (Zhang et al, 2014) and received signal strength indicator (RSSI) is the most used signal property in wireless indoor localization (Farid et al, 2013). In this paper, we address the problem of indoor mobile robot localization and tracking using WSNs.…”

Section: Introductionmentioning

confidence: 99%

Scanning method for indoor localization using the RSSI approach

Ahmad

Petković

Toepfer

2017

J. Sens. Sens. Syst.

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Abstract. This paper presents a scanning method for indoor mobile robot localization using the received signal strength indicator (RSSI) approach. The method eliminates the main drawback of the conventional fingerprint, whose database construction is time-consuming and which needs to be rebuilt every time a change in indoor environment occurs. It directly compares the column vectors of a kernel matrix and signal strength vector using the Euclidean distance as a metric. The highest resolution available in localization using a fingerprint is restricted by a resolution of a set of measurements performed prior to localization. In contrast, resolution using the scanning method can be easily changed using a denser grid of potential sources. Although slightly slower than the trilateration method, the scanning method outperforms it in terms of accuracy, and yields a reconstruction error of only 0.08 m averaged over 1600 considered source points in a room with dimensions 9.7 m × 4.7 m × 3 m. Its localization time of 0.39 s makes this method suitable for real-time localization and tracking.

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Tracking Mobile Robot in Indoor Wireless Sensor Networks

Cited by 12 publications

References 21 publications

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Modified Iterated Extended Kalman Filter for Mobile Cooperative Tracking System

Collision-free manipulation of a robotic arm using the MS Windows Kinect 3D optical system

Scanning method for indoor localization using the RSSI approach

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