14 research outputs found
Human motion retargeting to generate robot trajectory-replication of human-guided path
No full textIn this project we propose and study a methodology to generate a robot motion by the retargeting of human motion captured using an HTC Vive system. As a way to test the outcome, the human motion was obtained by the performer trying to mimic or follow a robot end effector in real time. Our task is to take the human motion as an input, use several techniques to process it and then reconstruct from it the motion that the performer was trying to achieve. We then compare this motion to the original motion recorded from the robot itself. The processing of the data consists of applying PCA to obtain a two-dimensional projection, computing the approximate period of the movements by the location of points, sampling average points at points evenly spaced in time along the cycle, and then using a Bspline to reconstruct a continuous, smooth and closed curve. A generic method is developed utilizing Python script, except for a first preprocessing step realized with Blender. The results of our study show that from a dataset with variation in the order of 10 to 20cm (the human motion) we obtain a result with an error in respect to the motion recorded from the robot in the order of 1 to 5cm.Grado en Ingeniería en Electrónica Industrial y Automátic
Automated Unsupervised 3D Tool-Path Generation Using Stacked 2D Image Processing Technique
No full textTool-path, feed-rate, and depth-of-cut of a tool determine the machining time, tool wear, power consumption, and realization costs. Before the commissioning and production, a preliminary phase of failure-mode identification and effect analysis allows for selecting the optimal machining parameters for cutting, which, in turn, reduces machinery faults, production errors and, ultimately, decreases costs. For this, scalable high-precision path generation algorithms requiring a low amount of computation might be advisable. The present work provides such a simplified scalable computationally low-intensive technique for tool-path generation. From a three dimensional (3D) digital model, the presented algorithm extracts multiple two dimensional (2D) layers. Depending on the required resolution, each layer is converted to a spatial image, and an algebraic analytic closed-form solution provides a geometrical tool path in Cartesian coordinates. The produced tool paths are stacked after processing all object layers. Finally, the generated tool path is translated into a machine code using a G-code generator algorithm. The introduced technique was implemented and simulated using MATLAB® pseudocode with a G-code interpreter and a simulator. The results showed that the proposed technique produced an automated unsupervised reliable tool-path-generator algorithm and reduced tool wear and costs, by allowing the selection of the tool depth-of-cut as an input
Real-Time Motion Tracking for Humans and Robots in a Collaborative Assembly Task
No full textHuman-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified
Review on Emission of Radiated Electromagnetic Fields from Train Pantograph Arcing
No full textPantograph arc is one of the most common and yet unavoidable difficulties in electrified railways. During winter the intensity of arcing increases due to ice layer on the overhead catenary wire. In AC traction system, the sinusoidal waveforms of the supply voltage and current distort due to pantograph arcing. It generates both conducted and radiated emission in a wide band. Both the DC component and higher order conducted and radiated emission increases with line speed. The amplitude of the DC voltage shows a wide variation concerning train speed, applied voltage, type of electrical load, the gap between the contact wire and the pantograph and current. In this paper, pantograph arcing and its effects on the railway vehicles are described. Sliding contact between the pantograph contact strips and the catenary contact wire is illustrated with the emphasis on the pantograph arcing. Arc characteristics, formation methods, extinction and resignation of the arc are studied. This paper presents a comprehensive review on pantograph arcing and its effects near radio-based mobile communications and other signaling instruments and some other related areas
Analysis and Evaluation of Energy Consumption over 4G Network
No full textThe mobile communication network cuts 0.5 percent of today’s global energy consumption. Among all other constrains, energy is the most critical concern to deploy any communication network. The demand of power for wireless networks increases dramatically. By applying three novel approaches, we can minimize the power consumption of 4G wireless networks such as optimal power scheduling (OPS) for base stations, packet delay scheduling (PDS) and sleep mode for variable traffic density (SMVTD). This paper presents energy consumption issues over the 4G wireless network and its associated constraints such as finding the optimal radio base stations to reduce the energy and cost for the entire system. Within same network coverage area if we optimize the number of base stations, then it will be a hallmark to minimize the energy cost. We have considered the various element concerning the 4G network and responsible parameters for energy consumption. We also studied the different mode of variable traffic density in 4G network
Industrial Human Activity Prediction and Detection Using Sequential Memory Networks
No full textPrediction of human activity and detection of subsequent actions is crucial for improving the interaction between humans and robots during collaborative operations. Deep-learning techniques are being applied to recognize human activities, including industrial applications. However, the lack of sufficient dataset in the industrial domain and complexities of some industrial activities such as screw driving, assembling small parts, and others affect the model development and testing of human activities. The InHard dataset (Industrial Human Activity Recognition Dataset) was recently published to facilitate industrial human activity recognition for better human-robot collaboration, which still lacks extended evaluation. We propose an activity recognition method using a combined convolutional neural network (CNN) and long short-term memory (LSTM) techniques to evaluate the InHard dataset and compare it with a new dataset captured in a lab environment. This method improves the success rate of activity recognition by processing temporal and spatial information. Accordingly, the accuracy of the dataset is tested using labeled lists of activities from IMU and video data. A model is trained and tested for nine low-level activity classes with approximately 400 samples per class. The test result shows 88% accuracy for IMU-based skeleton data, 77% for RGB spatial video, and 63% for RGB video-based skeleton. The result has been verified using a previously published region-based activity recognition. The proposed approach can be extended to push the cognition capability of robots in human-centric workplaces
Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration
No full textSemantic representation of motions in a human-robot collaborative environment is essential for agile design and development of digital twins (DT) towards ensuring efficient collaboration between humans and robots in hybrid work systems, e.g., in assembly operations. Dividing activities into actions helps to further conceptualize motion models for predicting what a human intends to do in a hybrid work system. However, it is not straightforward to identify human intentions in collaborative operations for robots to understand and collaborate. This paper presents a concept for semantic representation of human actions and intention prediction using a flexible task ontology interface in the semantic data hub stored in a domain knowledge base. This semantic data hub enables the construction of a DT with corresponding reasoning and simulation algorithms. Furthermore, a knowledge-based DT concept is used to analyze and verify the presented use-case of Human-Robot Collaboration in assembly operations. The preliminary evaluation showed a promising reduction of time for assembly tasks, which identifies the potential to i) improve efficiency reflected by reducing costs and errors and ultimately ii) assist human workers in improving decision making. Thus the contribution of the current work involves a marriage of machine learning, robotics, and ontology engineering into DT to improve human-robot interaction and productivity in a collaborative production environment
Sero-prevalence and risk factors study of brucellosis in small ruminants in Southern Zone of Tigray Region, Northern Ethiopia
No full textThis study reports a prevalence and risk factor survey of brucellosis in small ruminants in Southern Zone of Tigray Region, Northern Ethiopia between October 2011 and April 2012 to determine the sero-prevalence of small-ruminant brucellosis and to identify associated risk factors for the occurrence of disease in small ruminants under extensive production system. Multistage random sampling was followed to select locations, flocks, and individual animals. Laboratory analysis of serum samples provided sero-prevalence estimates for flocks and geographic location. Information on risk factors at the individual and flock level was obtained by examination of individual animal and a questionnaire interview to flock owners. The overall individual animal-level sero-prevalence of brucellosis in small ruminants was 3.5 % and flock level sero-prevalence was 28.3 %, and the within-flock sero-prevalence was ranged from 0 % to 22.2 % based on the Complement Fixation Test. Multivariable logistic regression showed that the major risk factors for flock level sero-positivity were flock size and abortion history. This study showed that small-ruminant brucellosis is prevalent in the study area. Larger flock size and history of previous abortion in the flock were major risk factors identified for sero-positivity of small-ruminant brucellosis
