Dynamic objects tracking with a mobile robot using passive UHF RFID tags

Liu, Ran; Huskic, Goran; Zell, Andreas

doi:10.1109/iros.2014.6943161

Cited by 18 publications

(13 citation statements)

References 10 publications

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“…RFID tags have unique IDs that can be used to identify an object. The phase reported from the RFID is a periodic function of the distance and has the ambiguity in positioning of an object [ 46 , 47 ]. The phase difference can be used to infer the moving velocity of an object.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

Wang

Liu

et al. 2018

Sensors

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RFID (Radio Frequency Identification) offers a way to identify objects without any contact. However, positioning accuracy is limited since RFID neither provides distance nor bearing information about the tag. This paper proposes a new and innovative approach for the localization of moving object using a particle filter by incorporating RFID phase and laser-based clustering from 2d laser range data. First of all, we calculate phase-based velocity of the moving object based on RFID phase difference. Meanwhile, we separate laser range data into different clusters, and compute the distance-based velocity and moving direction of these clusters. We then compute and analyze the similarity between two velocities, and select K clusters having the best similarity score. We predict the particles according to the velocity and moving direction of laser clusters. Finally, we update the weights of the particles based on K clusters and achieve the localization of moving objects. The feasibility of this approach is validated on a Scitos G5 service robot and the results prove that we have successfully achieved a localization accuracy up to 0.25 m.

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Section: Introductionmentioning

confidence: 99%

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

Wang

Liu

et al. 2018

Sensors

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“…Those approaches have proven to be useful for robot exploration. For example, [19] uses a Bayesian model to tracks tags and move robot towards the most likely location, but still requires detecting the tag to plan its next movement in a reactive way. Zhang et al [2] propose small changes to Dynamic Window Approach (DWA) to move closer to obstacles where tags are supposed to be, following a fixed global plan covering the environment.…”

Section: B Rfid Technologymentioning

confidence: 99%

“…As last, the current battery charge is another fundamental criterion when deploying a robot with long-term autonomy in mind. Moreover, we also differ from existing exploration methods for RFID tags such as [2], [3], [19] because we do not have any a priory assumption on the tag location but only the map of the environment to explore.…”

Section: B Rfid Technologymentioning

confidence: 99%

Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery

Polvara

Fernández-Carmona

Neumann

et al. 2020

IEEE Robot. Autom. Lett.

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Automated exploration is one of the most relevant applications for autonomous robots. In this paper, we propose a novel online coverage algorithm called Next-Best-Sense (NBS), an extension of the Next-Best-View class of exploration algorithms which optimizes the exploration task balancing multiple criteria. NBS is applied to the problem of localizing all Radio Frequency Identification (RFID) tags with a mobile robot. We cast this problem as a coverage planning problem by defining a basic sensing operation-a scan with the RFID reader-as the field of "view" of the sensor. NBS evaluates candidate locations with a global utility function which combines utility values for travel distance, information gain, sensing time, battery status and RFID information gain, generalizing the use of Multi-Criteria Decision Making. We developed an RFID reader and tag model in the Gazebo simulator for validation. Experiments performed both in simulation and with a robot suggest that our NBS approach can successfully localize all the RFID tags while minimizing navigation metrics, such sensing operations, total traveling distance and battery consumption. The code developed is publicly available on the authors' repository 1 .

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“…Some researchers also focused on dynamic objects tracking using RFID tags. Liu et al [8] [19] combined RFID and laser sensor to follow a dynamic RFID tag with obstacle avoidance capabilities using a mobile robot. Their algorithm combines two kinematics models and a dual particle filter to achieve fast tracking of dynamic RFID tags.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Track Moving Object based on RFID and Laser Ranging Information

2020

KSII TIIS

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RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of K best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.

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Dynamic objects tracking with a mobile robot using passive UHF RFID tags

Cited by 18 publications

References 10 publications

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

Moving Object Localization Based on UHF RFID Phase and Laser Clustering

Next-Best-Sense: A Multi-Criteria Robotic Exploration Strategy for RFID Tags Discovery

An Innovative Approach to Track Moving Object based on RFID and Laser Ranging Information

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