Correcting of the unexpected localization measurementfor indoor automatic mobile robot transportation basedon 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.