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Localization and tracking algorithm for mobile robot based on wireless sensor network

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PurposeThe purpose of this paper is to describe a low‐cost mobile robot localization and navigation system based on wireless sensor network (WSN). This can be used in extreme environments such as mines, hostile areas, and other places where static nodes cannot be deployed directly.Design/methodology/approachThe distance between robot and beacon nodes is estimated by received signal strength indicator (RSSI). The adaptive Kalman filter algorithm is used to eliminate noise from the RSSI signal. Then, the paper gets the mobile robot's status information which includes position, velocity, and acceleration. In order to improve the real‐time performance of systems and reduce the computation overhead of robot's CPU, the paper chooses a beacon node by some certain rules to implement the localization program. The robot and beacon nodes use the CC2430 as the communication and data‐processing unit.FindingsThe experiment shows it has the characteristics of good position precision, is easily realized, and inexpensive.Research limitations/implicationsThat the system needs so many beacon nodes, is the main limitation.Practical implicationsA very useful and low‐cost method for mobile robot localization and navigation.Originality/valueThe innovative aspect of the paper is that it attempts an analysis of the localization system based on WSN, by attempting to analyze and explain the model of localization system which includes robot and beacon nodes.
Title: Localization and tracking algorithm for mobile robot based on wireless sensor network
Description:
PurposeThe purpose of this paper is to describe a low‐cost mobile robot localization and navigation system based on wireless sensor network (WSN).
This can be used in extreme environments such as mines, hostile areas, and other places where static nodes cannot be deployed directly.
Design/methodology/approachThe distance between robot and beacon nodes is estimated by received signal strength indicator (RSSI).
The adaptive Kalman filter algorithm is used to eliminate noise from the RSSI signal.
Then, the paper gets the mobile robot's status information which includes position, velocity, and acceleration.
In order to improve the real‐time performance of systems and reduce the computation overhead of robot's CPU, the paper chooses a beacon node by some certain rules to implement the localization program.
The robot and beacon nodes use the CC2430 as the communication and data‐processing unit.
FindingsThe experiment shows it has the characteristics of good position precision, is easily realized, and inexpensive.
Research limitations/implicationsThat the system needs so many beacon nodes, is the main limitation.
Practical implicationsA very useful and low‐cost method for mobile robot localization and navigation.
Originality/valueThe innovative aspect of the paper is that it attempts an analysis of the localization system based on WSN, by attempting to analyze and explain the model of localization system which includes robot and beacon nodes.

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