scholarly journals Application of a Vision-Based Single Target on Robot Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1829
Author(s):  
Jing Yu ◽  
Wensong Jiang ◽  
Zai Luo ◽  
Li Yang
Keyword(s):  
Image Matching ◽  
Circular Ring ◽  
Fiducial Marker ◽  
Experimental Result ◽  
Long Distance ◽  
Translation Error ◽  
Single Target ◽  
Robot Positioning ◽  
Positioning Method ◽  
Average Rotation

In this paper, we propose a Circular-ring visual location marker based on a global image-matching model to improve the positioning ability in the fiducial marker system of a single-target mobile robot. The unique coding information is designed according to the cross-ratio invariance of the projective theorem. To verify the accuracy of full 6D pose estimation using the Circular-ring marker, a 6 degree of freedom (DoF) robotic arm platform is used to design a visual location experiment. The experimental result shows in terms of small resolution images, different size markers, and long-distance tests that our proposed robot positioning method significantly outperforms AprilTag, ArUco, and Checkerboard. Furthermore, through a repeatable robot positioning experiment, the results indicated that the proposed Circular-ring marker is twice as accurate as the fiducial marker at 2–4 m. In terms of recognition speed, the Circular-ring marker processes a frame within 0.077 s. When the Circular-ring marker is used for robot positioning at 2–4 m, the maximum average translation error of the Circular-ring marker is 2.19, 3.04, and 9.44 mm. The maximum average rotation error is also 1.703°, 1.468°, and 0.782°.

scholarly journals Cobot User Frame Calibration: Evaluation and Comparison between Positioning Repeatability Performances Achieved by Traditional and Vision-Based Methods

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 45
Author(s):  
Roberto Pagani ◽  
Cristina Nuzzi ◽  
Marco Ghidelli ◽  
Alberto Borboni ◽  
Matteo Lancini ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Production Line ◽  
Calibration Method ◽  
Fiducial Marker ◽  
Positioning System ◽  
Local Calibration ◽  
Calibration Methods ◽  
Robot Positioning ◽  
Good Repeatability ◽  
Calibration Time

Since cobots are designed to be flexible, they are frequently repositioned to change the production line according to the needs; hence, their working area (user frame) needs to be often calibrated. Therefore, it is important to adopt a fast and intuitive user frame calibration method that allows even non-expert users to perform the procedure effectively, reducing the possible mistakes that may arise in such contexts. The aim of this work was to quantitatively assess the performance of different user frame calibration procedures in terms of accuracy, complexity, and calibration time, to allow a reliable choice of which calibration method to adopt and the number of calibration points to use, given the requirements of the specific application. This has been done by first analyzing the performances of a Rethink Robotics Sawyer robot built-in user frame calibration method (Robot Positioning System, RPS) based on the analysis of a fiducial marker distortion obtained from the image acquired by the wrist camera. This resulted in a quantitative analysis of the limitations of this approach that only computes local calibration planes, highlighting the reduction of performances observed. Hence, the analysis focused on the comparison between two traditional calibration methods involving rigid markers to determine the best number of calibration points to adopt to achieve good repeatability performances. The analysis shows that, among the three methods, the RPS one resulted in very poor repeatability performances (1.42 mm), while the three and five points calibration methods achieve lower values (0.33 mm and 0.12 mm, respectively) which are closer to the reference repeatability (0.08 mm). Moreover, comparing the overall calibration times achieved by the three methods, it is shown that, incrementing the number of calibration points to more than five, it is not suggested since it could lead to a plateau in the performances, while increasing the overall calibration time.

BUILDING MAP AND POSITIONING SYSTEM FOR INDOOR ROBOT BASED ON PLAYER

2011 ◽  
Vol 08 (04) ◽  
pp. 281-290
Author(s):  
BIN WANG ◽  
WEI LU ◽  
BIN KONG
Keyword(s):  
Mobile Robot ◽  
Positioning System ◽  
Map Building ◽  
Adaptive Monte Carlo ◽  
Indoor Mobile Robot ◽  
Robot Positioning ◽  
Positioning Method ◽  
Dynamic Map ◽  
Slam Algorithm ◽  
Number Of Particles

In this paper, we have proposed a map-building and positioning method for an indoor mobile robot based on the open source platform Player. First, the DP-SLAM algorithm is transplanted to the Player and used to build the dynamic offline map. This would reduce the errors and constraints caused by manual map building. Second, the KLD-Sampling Adaptive Monte Carlo Locating (KLD-AMCL) algorithm is introduced to reduce the number of particles required in locating. Meanwhile, higher accuracy of localization is achieved through calculating the MLE and the real posterior KL distance. Finally, an indoor mobile robot positioning system is built by combining the Player platform, dynamic map building and KLD-AMCL algorithm. Experimental results show that the proposed system has better environmental adaptability and higher positioning accuracy.

Study on image matching of 3D measurement system on dynamic object in long distance

2006 ◽  
Author(s):  
Xiaoling Zhang ◽  
Yuchi Lin ◽  
Bo Wu ◽  
Meirong Zhao ◽  
Yinguo Huang
Keyword(s):  
Measurement System ◽  
Image Matching ◽  
3D Measurement ◽  
Dynamic Object ◽  
Long Distance ◽  
3D Measurement System

scholarly journals Indoor Dead Reckoning Localization Using Ultrasonic Anemometer with IMU

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Woojin Seo ◽  
Kwang-Ryul Baek
Keyword(s):  
Mobile Robot ◽  
Dead Reckoning ◽  
Measurement Unit ◽  
Velocity Data ◽  
Short Period ◽  
Instantaneous Position ◽  
Ultrasonic Anemometer ◽  
Robot Positioning ◽  
Positioning Method ◽  
Angular Velocities

Dead reckoning is an important aspect of estimating the instantaneous position of a mobile robot. An inertial measurement unit (IMU) is generally used for dead reckoning because it measures triaxis acceleration and triaxis angular velocities in order to estimate the position of the mobile robot. Positioning with inertial data is reasonable for a short period of time. However, the velocity, position, and attitude errors increase over time. Much research has been conducted in ways to reduce these errors. To position a mobile robot, an absolute positioning method can be combined with dead reckoning. The performance of a combined positioning method can be improved based on improvement in dead reckoning. In this paper, an ultrasonic anemometer is used to improve the performance of dead reckoning when indoors. A new approach to the equation of an ultrasonic anemometer is proposed. The ultrasonic anemometer prevents divergence of the mobile robot’s velocity. To position a mobile robot indoors, the ultrasonic anemometer measures the relative movement of air while the robot moves through static air. Velocity data from the ultrasonic anemometer and the acceleration and angular velocity data from the IMU are combined via Kalman filter. Finally we show that the proposed method has the performance with a positioning method using encoders on a good floor condition.

An Improved Method for Mobile Robot Localization Based on Passive RFID System

2012 ◽  
Vol 190-191 ◽  
pp. 651-655
Author(s):  
Qing Yang ◽  
Hong Yi Wang ◽  
Jian Cheng Li ◽  
Rong Jun Shen
Keyword(s):  
Mobile Robot ◽  
Rfid Tags ◽  
Positioning System ◽  
Robot Localization ◽  
Rfid Technology ◽  
Improved Method ◽  
Robot Positioning ◽  
Positioning Method ◽  
Mobile Robot Localization ◽  
Passive Rfid

RFID technology has been widely used in mobile robot positioning system for its unique advantages. RFID tags store their unique positions which are placed on the ceiling or the floor. The mobile robot carries a RFID reader which reads the RFID tags to position itself. In this paper, a new method for mobile robot localization is proposed, and the equations to calculate the position of the mobile robot are given. Finally, the experiment results show that compared to conventional positioning method, the proposed method can effectively improve the positioning accuracy of the mobile robot.

PARAMETER ANALYSIS OF A REMOTELY CONTROLLED HOME MONITORING SYSTEM USING MOBILE ROBOT VIA CAMERA

2017 ◽  
Vol 79 (2) ◽  
Author(s):  
Mariam Md Ghazaly ◽  
Ho Carl Choon ◽  
Mohd Amran Md Ali ◽  
Zulkeflee Abdullah ◽  
Soo Kok Yew ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Monitoring System ◽  
Robot Control ◽  
Pulse Width Modulation ◽  
Detection System ◽  
Home Monitoring ◽  
Obstacle Detection ◽  
Parameter Analysis ◽  
Experimental Result ◽  
Long Distance

In this paper, the performance and prototype of a remotely-controlled home monitoring mobile robot for security and surveillance purposes were discussed. Home monitoring system has been one of the basic infrastructures that is being used in most of the residential compound. However, traditional CCTV system, which requires supporting surfaces and high equipment cost, has forced human to search for an alternative. Thus, this project provided a more flexibility and mobility to the home monitoring system, which consisted of an obstacle detection system and a camera. After discussing the conception of the project, as part of the experiment aspect method, experiment setup and result were presented. In this paper, the objectives also looked into the sensitivity of the obstacle avoidance system, to design and develop a remotely-controlled home monitoring robot, to design and develop a networking system for long distance robot control and to analyze the performance of the motor in terms of pulse width modulation (PWM). In conclusion, the experimental result proved that the proposed project was successfully developed with detailed supporting data.

Robot Positioning Method Using Visual Tracking with Online Correction

2020 ◽  
Vol 2020 (0) ◽  
pp. 2P1-N04
Author(s):  
Ryuya TERAMOTO ◽  
Shunsuke OTA ◽  
Toshiyuki YASUDA ◽  
Mitsuru JINDAI
Keyword(s):  
Visual Tracking ◽  
Online Correction ◽  
Robot Positioning ◽  
Positioning Method

Node Localization Technology in Wireless Sensor Networks for Coal Mine Security Monitoring

2013 ◽  
Vol 748 ◽  
pp. 560-564
Author(s):  
Guang Xing Zhou ◽  
Yun Tao Yue ◽  
Ming Xue Li ◽  
Yu Guang Chen
Keyword(s):  
Low Cost ◽  
Node Localization ◽  
Long Distance ◽  
Simulation Experiments ◽  
Security Monitoring ◽  
Communication Module ◽  
Positioning Method ◽  
Distance Difference ◽  
Location Algorithm ◽  
Beacon Node

This paper proposed a location algorithm based on nodes distance difference, it gets the distance difference from the blind node to every beacon node by wireless communication module Rf grouping, then, makes use of the improved Trilateral positioning method to work out Node coordinates, so as to realize the node localization of long-distance range, precision controlled, low-cost, and to analyze the positioning performance. The simulation experiments shows this algorithm has high feasibility and positioning performance, and it is also a complement of WSN Positioning technology.

scholarly journals SL-BiLSTM: A Signal-Based Bidirectional LSTM Network for Over-the-Horizon Target Localization

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wanting Yu ◽  
Hongyi Yu ◽  
Ding Wang ◽  
Jianping Du ◽  
Mengli Zhang
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Technology ◽  
Long Distance ◽  
Position Information ◽  
Performance Loss ◽  
Low Snr ◽  
Regularization Techniques ◽  
Positioning Method ◽  
Lstm Network

Deep learning technology provides novel solutions for localization in complex scenarios. Conventional methods generally suffer from performance loss in the long-distance over-the-horizon (OTH) scenario due to uncertain ionospheric conditions. To overcome the adverse effects of the unknown and complex ionosphere on positioning, we propose a deep learning positioning method based on multistation received signals and bidirectional long short-term memory (BiLSTM) network framework (SL-BiLSTM), which refines position information from signal data. Specifically, we first obtain the form of the network input by constructing the received signal model. Second, the proposed method is developed to predict target positions using an SL-BiLSTM network, consisting of three BiLSTM layers, a maxout layer, a fully connected layer, and a regression layer. Then, we discuss two regularization techniques of dropout and randomization which are mainly adopted to prevent network overfitting. Simulations of OTH localization are conducted to examine the performance. The parameters of the network have been trained properly according to the scenario. Finally, the experimental results show that the proposed method can significantly improve the accuracy of OTH positioning at low SNR. When the number of training locations increases to 200, the positioning result of SL-BiLSTM is closest to CRLB at high SNR.

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