Development of an assistive patient mobile system for hospital environments

This paper presents an assistive patient mobile system for hospital environments, which focuses on transferring the patient without nursing help. The system is a combination of an advanced hospital bed and an autonomous navigating robot. This intelligent bed can track the robot and routinely navigates and communicates with the bed. The work centralizes in building a structure, hardware design and robot detection and tracking algorithms by using laser range finder. The assistive patient mobile system has been tested and the real experiments are shown with a high performance of reliability and practicality. The accuracy of the method proposed in this paper is 91% for the targeted testing object with the error rate of classification by 6%. Additionally, a comparison between our method and a related one is also described including the comparison of results. © 2013 IEEE

A novel target following solution for the electric powered hospital bed

© 2015 IEEE. The paper proposes a novel target following solution for an electric powered hospital bed. First, an improved real-time decoupling multivariable control strategy is introduced to stabilize the overall system during its operation. Environment laser-based data are then collected and pre-processed before engaging a neural network classifier for target detection. Finally, a high-level control algorithm is implemented to guarantee safety condition while the hospital bed tracks its target. The proposed solution is successfully validated through real-time experiments

Using a Deep Learning Model on Images to Obtain a 2D Laser People Detector for a Mobile Robot

Recent improvements in deep learning techniques applied to images allow the detection of people with a high success rate. However, other types of sensors, such as laser rangefinders, are still useful due to their wide field of vision and their ability to operate in different environments and lighting conditions. In this work we use an interesting computational intelligence technique such as the deep learning method to detect people in images taken by a mobile robot. The masks of the people in the images are used to automatically label a set of samples formed by 2D laser range data that will allow us to detect the legs of people present in the scene. The samples are geometric characteristics of the clusters built from the laser data. The machine learning algorithms are used to learn a classifier that is capable of detecting people from only 2D laser range data. Our people detector is compared to a state-of-the-art classifier. Our proposal achieves a higher value of F1 in the test set using an unbalanced dataset. To improve accuracy, the final classifier has been generated from a balanced training set. This final classifier has also been evaluated using a test set in which we have obtained very high accuracy values in each class. The contribution of this work is 2-fold. On the one hand, our proposal performs an automatic labeling of the samples so that the dataset can be collected under real operating conditions. On the other hand, the robot can detect people in a wider field of view than if we only used a camera, and in this way can help build more robust behaviors.This work has been supported by the Spanish Government TIN2016- 76515-R Grant, supported with Feder funds

Three-Dimensional Reconstruction and Modeling Using Low-Precision Vision Sensors for Automation and Robotics Applications in Construction

Automation and robotics in construction (ARC) has the potential to assist in the performance of several mundane, repetitive, or dangerous construction tasks autonomously or under the supervision of human workers, and perform effective site and resource monitoring to stimulate productivity growth and facilitate safety management. When using ARC technologies, three-dimensional (3D) reconstruction is a primary requirement for perceiving and modeling the environment to generate 3D workplace models for various applications. Previous work in ARC has predominantly utilized 3D data captured from high-fidelity and expensive laser scanners for data collection and processing while paying little attention of 3D reconstruction and modeling using low-precision vision sensors, particularly for indoor ARC applications. This dissertation explores 3D reconstruction and modeling for ARC applications using low-precision vision sensors for both outdoor and indoor applications. First, to handle occlusion for cluttered environments, a joint point cloud completion and surface relation inference framework using red-green-blue and depth (RGB-D) sensors (e.g., Microsoft® Kinect) is proposed to obtain complete 3D models and the surface relations. Then, to explore the integration of prior domain knowledge, a user-guided dimensional analysis method using RGB-D sensors is designed to interactively obtain dimensional information for indoor building environments. In order to allow deployed ARC systems to be aware of or monitor humans in the environment, a real-time human tracking method using a single RGB-D sensor is designed to track specific individuals under various illumination conditions in work environments. Finally, this research also investigates the utilization of aerially collected video images for modeling ongoing excavations and automated geotechnical hazards detection and monitoring. The efficacy of the researched methods has been evaluated and validated through several experiments. Specifically, the joint point cloud completion and surface relation inference method is demonstrated to be able to recover all surface connectivity relations, double the point cloud size by adding points of which more than 87% are correct, and thus create high-quality complete 3D models of the work environment. The user-guided dimensional analysis method can provide legitimate user guidance for obtaining dimensions of interest. The average relative errors for the example scenes are less than 7% while the absolute errors less than 36mm. The designed human worker tracking method can successfully track a specific individual in real-time with high detection accuracy. The excavation slope stability monitoring framework allows convenient data collection and efficient data processing for real-time job site monitoring. The designed geotechnical hazard detection and mapping methods enable automated identification of landslides using only aerial video images collected using drones.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138626/1/yongxiao_1.pd