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BLE beacon-based floor detection for mobile robots in a multi-floor automation laboratory

Transportation Safety and Environment 2024年第2期6卷 114-121页

作者： Haiping Wu Hui Liu Thomas Roddelkopf kerstin thurow Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangshaHunan 410075China Center of Life Science Automation(celisca) 18119 Rostoc kGerman y Institute of Automation(IAT) University Rostock18119 RostockGermany

As an important task of multi-floor localization,floor detection has elicited great *** infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor ***,most floor detection research tends to focus on data modelling but pays little attention to the data collection system,which is the basis of wireless infrastructure-based floor *** fact,the floor detection task can be greatly simplified with proper data collection system *** this paper,a floor detection solution is developed in a multi-floor life science automation lab.A data collection system consisting of BLE beacons,a receiver node and an Internet of Things(IoT)cloud is *** features of the BLE beacon under different settings are evaluated in detail.A mean filter is designed to deal with the fluctuation of the received signal strength indicator data.A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection *** time delay and floor detection accuracy under different settings are ***,floor detection is evaluated on the H20 multi-floor transportation *** sensor nodes are installed on the robot at different *** floor detection performance with different installation heights is *** experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.

关键词： BLE beacon floor detection multi-floor automation transportation robot

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维普期刊数据库

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Correcting of the unexpected localization measurement for indoor automatic mobile robot transportation based on a neural network

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Transportation Safety and Environment 2024年第2期6卷 24-35页

作者： Jiahao Huang Steffen Jung inger Hui Liu kerstin thurow Center for Life Science Automation(CELISCA) University of RostockRostock 18119Germany Institute of Automation University of RostockRostock 18119Germany Institute of Artificial Intelligence&Robotics(IAIR) School of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory ***,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are *** can compromise the accuracy of the robot’s localization,which is crucial for the safety of staff,robots and the laboratory.A novel time-series predicting model based on the data processing method is proposed to handle the unexpected localization measurement of mobile robots in laboratory *** proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile *** experimental results demonstrate the effectiveness of this proposed method.

关键词： mobile robots laboratory automation indoor localization neural network

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BLE beacons for sample position estimation in a life science automation laboratory

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Transportation Safety and Environment 2024年第3期6卷 111-123页

作者： Haiping Wu Steffen Junginger Thomas Roddelkopf Hui Liu kerstin thurow Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075HunanChina Institute of Automation(IAT) University of Rostock18119 RostockGermany Center of Life Science Automation(celisca) University of Rostock18119 RostockGermany

Estimation of the sample position is essential for working process monitoring and management in the life science automation lab*** low-energy(BLE)beacons have the advantages of low price,small size and low energy consumption,which make them a promising solution for sample position estimation in the automated *** fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)***,most of the research depends on intensive beacon *** estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated *** complexity of the life science automation laboratory environment brings challenges to the traditional path loss model(PLM),which is a widely used radio wave propagation model-based proximity estimation *** this paper,BLE sensing devices for sample position estimation are *** BLE beacon-based proximity estimation is discussed in the framework of machine learning,in which the support vector regression(SVR)is utilized to model the nonlinear relationship between the RSS data and distance,and the Kalman filter is utilized to decrease the RSS data *** experimental results over different environments indicate that the SVR outperforms the PLM significantly,and provides 1 m absolute errors for more than 95%of the testing *** Kalman filter brings benefits to stable distance *** from proximity-based sample position estimation,the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

关键词： BLE beacon position estimation proximity estimation received signal strength(RSS) support vector regression(SVR) life science automation laboratory

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Transportation robot battery power forecasting based on bidirectional deep-learning method

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Transportation Safety and Environment 2019年第3期1卷 205-211页

作者： kerstin thurow Chao Chen Steffen Junginger Norbert Stoll Hui Liu Center for Life Science Automation University of RostockFriedrich-Barnewitz-StraBe 818119 RostockGermany School of Traffic and Transportation Engineering Central South UniversityChangsha410075 HunanChina and Institute of Automation University of RostockRichard-Wagner-StraBe 3118119 RostockGermany

This paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning *** the proposed model,the on-board battery power data is measured and transmitted.A WPD(wavelet packet decomposition)algorithm is employed to decompose the original collected non-stationary series into several relatively more stable *** each subseries,a deep learning–based predictor–bidirectional long short-term memory(BiLSTM)–is constructed to forecast the battery power voltage from one step to three steps *** experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting *** obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged,providing effective support for the safe use of transportation robots.

关键词： robotic power management transportation robot time series forecasting wavelet packet decomposition bidirectional long short-term memory

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Deep Learning for Visual SLAM in Transportation Robotics:A review

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Transportation Safety and Environment 2019年第3期1卷 177-184页

作者： Chao Duan Steffen Junginger Jiahao Huang Kairong Jin kerstin thurow Institute of Artificial Intelligence&Robotics(IAIR) School of Traffic&Transportation EngineeringCentral South University410075 China Institute of Automation University Rostock18119 Germany 3Center for Life Science AutomationUniversity of Rostock18119 Germany

Visual SLAM(Simultaneously Localization and Mapping)is a solution to achieve localization and mapping of robots *** achievements have been made during the past decades,geography-based methods are becoming more and more successful in dealing with static ***,they still cannot handle a challenging *** the great achievements of deep learning methods in the field of computer vision,there is a trend of applying deep learning methods to visual *** this paper,the latest research progress of deep learning applied to the field of visual SLAM is *** outstanding research results of deep learning visual odometry and deep learning loop closure detect are ***,future development directions of visual SLAM based on deep learning is prospected.

关键词： deep learning visual SLAM transportation robotics mobile robots

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Multi-floor laboratory transportation technologies based on intelligent mobile robots

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Transportation Safety and Environment 2019年第1期1卷 37-53页

作者： kerstin thurow Lei Zhang Hui Liu Steffen Junginger Norbert Stoll Jiahao Huang Center for Life Science Automation(CELISCA) University of RostockRostock 18119Germany Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic&Transportation EngineeringCentral South UniversityChangsha 410075HunanChina Institute of Automation University of RostockRostock 18119Germany

Transportation technologies for mobile robots include indoor navigation,intelligent collision avoidance and target *** paper discusses the research process and development of these interrelated *** efficient multi-floor laboratory transportation system for mobile robots developed by the group at the Center for Life Science Automation(CELISCA)is then *** system is integrated with the multi-floor navigation and intelligent collision avoidance systems,as well as a labware manipulation system.A multi-floor navigation technology is proposed,comprising sub-systems for mapping and localization,path planning,door control and elevator *** on human-robot interaction technology,a collision avoidance system is proposed that improves the navigation of the robots and ensures the safety of the transportation *** and placing operation technologies using the dual arms of the robots are investigated and integrated into the multi-floor transportation *** proposed transportation system is installed on the H20 mobile robots and tested at the CELISCA *** results show that the proposed system can ensure the mobile robots are successful when performing multi-floor laboratory transportation tasks.

关键词： robots mobile robots laboratory transportation navigation

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将理想变为现实打造自动实验室的新方案

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实验与分析 2017年第2期 53-55页

作者： kerstin thurow 罗斯托克大学生命科学自动化中心

现如今，全自动检测分析实验室仍处于初级阶段。罗斯托克大学生命科学自动化中心正在研发未来型实验室方案并进行着不断尝试与摸索。

关键词：分析实验室理想全自动检测生命科学未来型自动化

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