scholarly journals Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 717
Author(s):  
Farhad Shamsfakhr ◽  
Andrea Motroni ◽  
Luigi Palopoli ◽  
Alice Buffi ◽  
Paolo Nepa ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Uhf Rfid ◽  
Missing Measurements ◽  
Kalman Smoother ◽  
Vehicle Trajectories ◽  
Angular Velocities ◽  
Reference Tags ◽  
Entire Trajectory ◽  
Method Accuracy ◽  
Mean Square Errors

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.

Indoor Localization Using the Reference Tags and Phase of Passive UHF-RFID Tags

2017 ◽  
Vol 13 (2) ◽  
pp. 69-82 ◽  
Author(s):  
He Xu ◽  
Ye Ding ◽  
Peng Li ◽  
Ruchuan Wang
Keyword(s):  
Production Management ◽  
Indoor Localization ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Passive Uhf Rfid ◽  
Hardware System ◽  
Reference Tags ◽  
Software And Hardware ◽  
Collaborative Information System ◽  
Time And Energy

In recent years, indoor position has been an important role in many applications, such as production management, store management and shelves in supermarket or library. Much time and energy are exhausted because one object cannot be quickly and accurately located. Traditional indoor position systems have some problems, such as complicated software and hardware system, inaccurate position and high time complexity. In this paper, the authors propose an RFID-based collaborative information system, Tagrom, for indoor localization using COTS RFID readers and tags. Unlike former methods, Tagrom works with reference tags and phase of Passive UHF-RFID tags, which improves traditional distribution of reference tags and utilize RF phase replace of traditional RSSI or multipath profile to determine the position of target RFID tags.

A Single Forward-Velocity Control Signal for Stochastic Source Seeking With Multiple Nonholonomic Vehicles

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Paul Frihauf ◽  
Shu-Jun Liu ◽  
Miroslav Krstic
Keyword(s):  
Autonomous Vehicles ◽  
Small Neighborhood ◽  
Control Signal ◽  
Extremum Seeking ◽  
Velocity Control ◽  
Extremum Seeking Control ◽  
Spatially Distributed ◽  
Angular Velocities ◽  
Nonholonomic Vehicles ◽  
Signal Field

With a single stochastic extremum seeking control signal, we steer multiple autonomous vehicles, modeled as nonholonomic unicycles, toward the maximum of an unknown, spatially distributed signal field. The vehicles, whose angular velocities are constant and distinct, travel at the same forward velocity, which is controlled by the stochastic extremum seeking controller. To determine the vehicles’ velocity, the controller uses measurements of the signal field at the respective vehicle positions and excitation based on filtered white noise. The positions of the vehicles are not measured. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source and provide a numerical example to illustrate the effectiveness of the algorithm.

Validity of the Top-Down Approach of Inverse Dynamics Analysis in Fast and Large Rotational Trunk Movements

2012 ◽  
Vol 28 (4) ◽  
pp. 420-430 ◽  
Author(s):  
Yoichi Iino ◽  
Takeji Kojima
Keyword(s):  
Inverse Dynamics ◽  
Axial Rotation ◽  
Dynamics Analysis ◽  
Top Down ◽  
Trunk Movements ◽  
Flexion Extension ◽  
Angular Velocities ◽  
Inverse Dynamics Analysis ◽  
Three Body ◽  
Mean Square Errors

This study investigated the validity of the top-down approach of inverse dynamics analysis in fast and large rotational movements of the trunk about three orthogonal axes of the pelvis for nine male collegiate students. The maximum angles of the upper trunk relative to the pelvis were approximately 47°, 49°, 32°, and 55° for lateral bending, flexion, extension, and axial rotation, respectively, with maximum angular velocities of 209°/s, 201°/s, 145°/s, and 288°/s, respectively. The pelvic moments about the axes during the movements were determined using the top-down and bottom-up approaches of inverse dynamics and compared between the two approaches. Three body segment inertial parameter sets were estimated using anthropometric data sets (Ae et al., Biomechanism 11, 1992; De Leva, J Biomech, 1996; Dumas et al., J Biomech, 2007). The root-mean-square errors of the moments and the absolute errors of the peaks of the moments were generally smaller than 10 N·m. The results suggest that the pelvic moment in motions involving fast and large trunk movements can be determined with a certain level of validity using the top-down approach in which the trunk is modeled as two or three rigid-link segments.

scholarly journals Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

2021 ◽  
Vol 13 (3) ◽  
pp. 506
Author(s):  
Xiaohu Lin ◽  
Fuhong Wang ◽  
Bisheng Yang ◽  
Wanwei Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Point Cloud ◽  
Autonomous Vehicle ◽  
Satellite System ◽  
Initial Position ◽  
Block Matching ◽  
Pose Prediction ◽  
Vehicle Localization ◽  
Accurate Localization ◽  
Mean Square Errors

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively.

scholarly journals Localization of passive UHF RFID Labels with Kalman Filter

2012 ◽  
Vol 10 ◽  
pp. 119-125 ◽  
Author(s):  
T. Nick ◽  
J. Götze
Keyword(s):  
Kalman Filter ◽  
Radio Frequency Identification ◽  
Unscented Kalman Filter ◽  
Uhf Rfid ◽  
Received Signal Strength Indicator ◽  
Localization Accuracy ◽  
Passive Uhf Rfid ◽  
Reader Antenna ◽  
Reference Tags ◽  
Frequency Identification

Abstract. Localization via Radio Frequency Identification (RFID) is frequently used in different applications nowadays. It has the advantage that next to its ostensible purpose of identifying objects via their unique IDs it can simultaneously be used for the localization of these objects. In this work it is shown how Received Signal Strength Indicator (RSSI) measurements at different antennae of a passive UHF RFID label can be combined for localization. The localization is only done based on the RSSI measurements and a Kalman Filter (KF). Because of non-linearities in the measurement function it is necessary to incorporate an Extended Kalman Filter (EKF) or an Unscented Kalman Filter (UKF) where simulations have shown that the UKF performs better than the EKF. Additionally to the selection of the filter there are different possibilities to increase the localization accuracy of the UKF: The advantages of using Reference Tags (RT) or more than one tag per trolley (relative positioning) in combination with an Unscented Kalman Filter are discussed and simulations results show that the localization error can be decreased significantly via these methods. Another possibility to increase the localization accuracy and in addition to achieve a more realistic simulation is the consideration of the angle between reader antenna and tag. Simulation results with the incorporation of different numbers of fixed antennae lead to the conclusion that this is a useful surplus in the localization.

scholarly journals Learning Control Policies of Driverless Vehicles from UAV Video Streams in Complex Urban Environments

2019 ◽  
Vol 11 (23) ◽  
pp. 2723
Author(s):  
Inder ◽  
Silva ◽  
Shi
Keyword(s):  
Autonomous Vehicles ◽  
City Planning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Urban Environments ◽  
Imitation Learning ◽  
Safe Driving ◽  
Traffic Conditions ◽  
Vehicle Trajectories ◽  
Local Experience

The way we drive, and the transport of today are going through radical changes. Intelligent mobility envisions to improve the efficiency of traditional transportation through advanced digital technologies, such as robotics, artificial intelligence and Internet of Things. Central to the development of intelligent mobility technology is the emergence of connected autonomous vehicles (CAVs) where vehicles are capable of navigating environments autonomously. For this to be achieved, autonomous vehicles must be safe, trusted by passengers, and other drivers. However, it is practically impossible to train autonomous vehicles with all the possible traffic conditions that they may encounter. The work in this paper presents an alternative solution of using infrastructure to aid CAVs to learn driving policies, specifically for complex junctions, which require local experience and knowledge to handle. The proposal is to learn safe driving policies through data-driven imitation learning of human-driven vehicles at a junction utilizing data captured from surveillance devices about vehicle movements at the junction. The proposed framework is demonstrated by processing video datasets captured from uncrewed aerial vehicles (UAVs) from three intersections around Europe which contain vehicle trajectories. An imitation learning algorithm based on long short-term memory (LSTM) neural network is proposed to learn and predict safe trajectories of vehicles. The proposed framework can be used for many purposes in intelligent mobility, such as augmenting the intelligent control algorithms in driverless vehicles, benchmarking driver behavior for insurance purposes, and for providing insights to city planning.

Modified RSSI Technique for the Localization of Passive UHF RFID Tags in LOS Channels

2013 ◽  
Vol 5 (5) ◽  
pp. 645-651 ◽  
Author(s):  
Y. Duroc ◽  
G. Andia Vera ◽  
J. P. Garcia Martin
Keyword(s):  
Radio Frequency Identification ◽  
Simple Algorithm ◽  
Rfid Tags ◽  
Uhf Rfid ◽  
Unknown Parameters ◽  
Received Signal Strength Indicator ◽  
Passive Uhf Rfid ◽  
Reference Tags ◽  
Frequency Identification ◽  
Complex Propagation

This paper presents a new approach for improving the localization of passive ultra high frequency radio frequency identification (RFID) tags in line-of-sight channels using a received signal strength indicator (RSSI) technique. In practice, the complex propagation in the indoor channels and also the variability of some parameters of the RFID equipment itself introduces significant amount of errors when the operation of localization carries out the RSSI technique. Indeed, as the calculation is based on a trilateration, the incomplete knowledge of the propagation and some parameters of RFID tags leads to estimate distances which are wrong, and therefore the localization cannot be correct. In order to overcome this drawback, the proposed method takes into account the presence of unknown parameters relying on a dichotomous algorithm which includes probabilistic parameters. The presented simulation results are in good agreement with the expected theoretical results. Experimental results show that the proposed method strongly increases the accuracy of the estimated position of tags. Compared to other approaches based on the improvement of the RSSI technique, this method does not require too much complexity in terms of materials (no need for specific architecture or reference tags) and processing (fast and simple algorithm).

Sign in / Sign up

Export Citation Format

Share Document