将理想变为现实 打造自动实验室的新方案

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。

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

Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory.Bluetooth 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 laboratory.Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength(RSS)data.However,most of the research depends on intensive beacon installation.Proximity estimation,which depends entirely on one beacon,is more suitable for sample position estimation in large automated laboratories.The 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 method.In this paper,BLE sensing devices for sample position estimation are proposed.The 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 deviation.The 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 samples.The Kalman filter brings benefits to stable distance predictions.Apart 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.

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

As an important task of multi-floor localization,floor detection has elicited great attention.Wireless infrastructures like Wi-Fi and Bluetooth Low Energy(BLE)play important roles in floor detection.However,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 detection.In fact,the floor detection task can be greatly simplified with proper data collection system design.In 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 provided.The 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 tests.The time delay and floor detection accuracy under different settings are discussed.Finally,floor detection is evaluated on the H20 multi-floor transportation robot.Two sensor nodes are installed on the robot at different heights.The floor detection performance with different installation heights is discussed.The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5s.

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

The increasing use of mobile robots in laboratory settings has led to a higher degree of laboratory automation.However,when mobile robots move in laboratory environments,mechanical errors,environmental disturbances and signal interruptions are inevitable.This 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 environments.The proposed model serves as an auxiliary localization system that can accurately correct unexpected localization errors by relying solely on the historical data of mobile robots.The experimental results demonstrate the effectiveness of this proposed method.

Deep Learning for Visual SLAM in Transportation Robotics:A review

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

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

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 technique.In 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 subseries.For 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 ahead.Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model,which shows the highest forecasting accuracy.The 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.

Multi-floor laboratory transportation technologies based on intelligent mobile robots

Transportation technologies for mobile robots include indoor navigation,intelligent collision avoidance and target manipulation.This paper discusses the research process and development of these interrelated technologies.An efficient multi-floor laboratory transportation system for mobile robots developed by the group at the Center for Life Science Automation(CELISCA)is then introduced.This 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 operation.Based 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 process.Grasping and placing operation technologies using the dual arms of the robots are investigated and integrated into the multi-floor transportation system.The proposed transportation system is installed on the H20 mobile robots and tested at the CELISCA laboratory.The results show that the proposed system can ensure the mobile robots are successful when performing multi-floor laboratory transportation tasks.