scholarly journals Human Modeling for Efficient Predictive Collision Detection

2020 ◽  
Vol 22 ◽  
Author(s):  
Gabriel Streitmatter
Keyword(s):  
Collision Detection ◽  
Flexible Manufacturing ◽  
Dynamic Environments ◽  
The Body ◽  
Time Data ◽  
Look Ahead ◽  
Human Modeling ◽  
Sequential Samples ◽  
Swept Volume ◽  
Human Robot Collaboration

As demands on manufacturing rapidly evolve, flexible manufacturing is becoming more essential for acquiring the necessary productivity to remain competitive. An innovative approach to flexible manufacturing is the introduction of fenceless robotic manufacturing cells to acquire and leverage greater human-robot collaboration (HRC). This involves operations in which a human and a robot share a space, complete tasks together, and interact with each other. Such operations, however, pose serious safety concerns. Before HRC can become a viable possibility, robots must be capable of safely operating within and responding to events in dynamic environments. Furthermore, the robot must be able to do this quickly during online operation. This paper outlines an algorithm for predictive collision detection. This algorithm gives the robot the ability to look ahead at its trajectory, and the trajectories of other bodies in its environment and predict potential collisions. The algorithm approximates a continuous swept volume of any articulated body along its trajectory by taking only a few time sequential samples of the predicted orientations of the body and creating surfaces that patch the orientations together with Coons patches. Run time data collected on this algorithm suggest that the algorithm can accurately predict future collisions in under 30 ms.

Human-Robot Collaboration: A Predictive Collision Detection Approach for Operation Within Dynamic Environments

2020 ◽  
Author(s):  
Gabriel Streitmatter ◽  
Gloria Wiens
Keyword(s):  
Collision Detection ◽  
Production Efficiency ◽  
Situational Awareness ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Computationally Efficient ◽  
Motion Data ◽  
Multiple Test ◽  
Look Ahead ◽  
Detection Approach

Abstract Robots and humans closely working together within dynamic environments must be able to continuously look ahead and identify potential collisions within their ever-changing environment. To enable the robot to act upon such situational awareness, its controller requires an iterative collision detection capability that will allow for computationally efficient Proactive Adaptive Collaboration Intelligence (PACI) to ensure safe interactions. In this paper, an algorithm is developed to evaluate a robot’s trajectory, evaluate the dynamic environment that the robot operates in, and predict collisions between the robot and dynamic obstacles in its environment. This algorithm takes as input the joint motion data of predefined robot execution plans and constructs a sweep of the robot’s instantaneous poses throughout time. The sweep models the trajectory as a point cloud containing all locations occupied by the robot and the time at which they will be occupied. To reduce the computational burden, Coons patches are leveraged to approximate the robot’s instantaneous poses. In parallel, the algorithm creates a similar sweep to model any human(s) and other obstacles being tracked in the operating environment. Overlaying temporal mapping of the sweeps reveals anticipated collisions that will occur if the robot-human do not proactively modify their motion. The algorithm is designed to feed into a segmentation and switching logic framework and provide real-time proactive-n-reactive behavior for different levels of human-robot interactions, while maintaining safety and production efficiency. To evaluate the predictive collision detection approach, multiple test cases are presented to quantify the computational speed and accuracy in predicting collisions.

scholarly journals IoT-enabled directed acyclic graph in spark cluster

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Jahwan Koo ◽  
Nawab Muhammad Faseeh Qureshi ◽  
Isma Farah Siddiqui ◽  
Asad Abbas ◽  
Ali Kashif Bashir
Keyword(s):  
Distributed Computing ◽  
Real Time ◽  
Directed Acyclic Graph ◽  
Random Access ◽  
Heterogeneous Data ◽  
The Body ◽  
Computing Environment ◽  
Time Data ◽  
Acyclic Graph ◽  
Sensory Data

Abstract Real-time data streaming fetches live sensory segments of the dataset in the heterogeneous distributed computing environment. This process assembles data chunks at a rapid encapsulation rate through a streaming technique that bundles sensor segments into multiple micro-batches and extracts into a repository, respectively. Recently, the acquisition process is enhanced with an additional feature of exchanging IoT devices’ dataset comprised of two components: (i) sensory data and (ii) metadata. The body of sensory data includes record information, and the metadata part consists of logs, heterogeneous events, and routing path tables to transmit micro-batch streams into the repository. Real-time acquisition procedure uses the Directed Acyclic Graph (DAG) to extract live query outcomes from in-place micro-batches through MapReduce stages and returns a result set. However, few bottlenecks affect the performance during the execution process, such as (i) homogeneous micro-batches formation only, (ii) complexity of dataset diversification, (iii) heterogeneous data tuples processing, and (iv) linear DAG workflow only. As a result, it produces huge processing latency and the additional cost of extracting event-enabled IoT datasets. Thus, the Spark cluster that processes Resilient Distributed Dataset (RDD) in a fast-pace using Random access memory (RAM) defies expected robustness in processing IoT streams in the distributed computing environment. This paper presents an IoT-enabled Directed Acyclic Graph (I-DAG) technique that labels micro-batches at the stage of building a stream event and arranges stream elements with event labels. In the next step, heterogeneous stream events are processed through the I-DAG workflow, which has non-linear DAG operation for extracting queries’ results in a Spark cluster. The performance evaluation shows that I-DAG resolves homogeneous IoT-enabled stream event issues and provides an effective stream event heterogeneous solution for IoT-enabled datasets in spark clusters.

Simulationsgestützte Produktionsplanung flexibler Fertigungssysteme*/Simulation-based production planning of flexible manufacturing systems

2019 ◽  
Vol 109 (04) ◽  
pp. 242-249
Author(s):  
A. Selmaier ◽  
T. Donhauser ◽  
T. Lechler ◽  
J. Zeitler ◽  
J. Franke
Keyword(s):  
Production Planning ◽  
Flexible Manufacturing ◽  
Material Flow ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Flow Simulation ◽  
Limited Extent ◽  
Time Data ◽  
High Data ◽  
Simulation Based

Während sich das Verhalten starr verketteter Systeme relativ einfach mittels Materialflusssimulationen modellieren lässt, sind herkömmliche Simulationsansätze für flexible Fertigungssysteme aufgrund des hohen Datenerhebungs- sowie Parametrisieraufwands nur bedingt geeignet. Jedoch kann durch das automatische Übertragen von Echtzeitdaten in das Simulationsmodell der aktuelle Zustand solcher Systeme deutlich verbessert abgebildet werden. Der Beitrag stellt ein Konzept für die simulationsgestützte Produktionsplanung schnellveränderlicher Systeme vor.   While the behaviour of rigidly linked systems is relatively easy to model by means of material flow simulation, traditional simulation approaches are only suitable to a limited extent for flexible manufacturing systems due to the high data collection and parameterization effort. However, the use of real-time data can significantly improve the simulation of such systems. This paper presents an approach for simulation-based production planning of rapidly changing systems.

scholarly journals Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

2022 ◽  
Vol 11 (1) ◽  
pp. 1-27
Author(s):  
Luis F. C. Figueredo ◽  
Rafael De Castro Aguiar ◽  
Lipeng Chen ◽  
Thomas C. Richards ◽  
Samit Chakrabarty ◽  
...  
Keyword(s):  
Muscular Activity ◽  
The Body ◽  
Force Output ◽  
Content Type ◽  
Joint Torques ◽  
Shared Object ◽  
Muscular Load ◽  
Robot Configuration ◽  
Human Robot Collaboration ◽  
Muscular Effort

This work addresses the problem of planning a robot configuration and grasp to position a shared object during forceful human-robot collaboration, such as a puncturing or a cutting task. Particularly, our goal is to find a robot configuration that positions the jointly manipulated object such that the muscular effort of the human, operating on the same object, is minimized while also ensuring the stability of the interaction for the robot. This raises three challenges. First, we predict the human muscular effort given a human-robot combined kinematic configuration and the interaction forces of a task. To do this, we perform task-space to muscle-space mapping for two different musculoskeletal models of the human arm. Second, we predict the human body kinematic configuration given a robot configuration and the resulting object pose in the workspace. To do this, we assume that the human prefers the body configuration that minimizes the muscular effort. And third, we ensure that, under the forces applied by the human, the robot grasp on the object is stable and the robot joint torques are within limits. Addressing these three challenges, we build a planner that, given a forceful task description, can output the robot grasp on an object and the robot configuration to position the shared object in space. We quantitatively analyze the performance of the planner and the validity of our assumptions. We conduct experiments with human subjects to measure their kinematic configurations, muscular activity, and force output during collaborative puncturing and cutting tasks. The results illustrate the effectiveness of our planner in reducing the human muscular load. For instance, for the puncturing task, our planner is able to reduce muscular load by 69.5\% compared to a user-based selection of object poses.

The International Migration Review at 50: Reflecting on Half a Century of International Migration Research and Looking Ahead

2014 ◽  
Vol 48 (1_suppl) ◽  
pp. 3-36 ◽  
Author(s):  
Jennifer Lee ◽  
Jørgen Carling ◽  
Pia Orrenius
Keyword(s):  
International Migration ◽  
50Th Anniversary ◽  
The Body ◽  
International Migration Review ◽  
Migration Research ◽  
Look Ahead ◽  
Research Themes ◽  
Anniversary Issue ◽  
Policy Agendas ◽  
And Migration

When the International Migration Review was established half a century ago, international migration was a peripheral area of research, and migration issues were far less prominent on policy agendas than they are today. This essay introduces the 50th Anniversary Issue of the International Migration Review and begins by identifying seven main areas of change in migration research and migration trends during the journal's lifetime. Subsequently, we examine changes in the geographical distribution of authorship of IMR articles. We also explore the IMR's current positioning in the scientific landscape by analyzing citation relationships with other journals. The ten articles that make up the body of the special issue seek to advance the research frontier on international migration, covering diverse areas of the IMR's thematic scope. We account for how the papers were selected and present each one. In the final section of the article, we look ahead and suggest new frontiers in international migration research. Among the research themes that we foresee as increasingly important are connections between migration and inequality, and the growth of migration flows that are driven by humanitarian crises, but not accommodated by the international refugee regime.

IoT-Based Health Services Framework for Endless Ailment Administration at Remote Areas

2020 ◽  
pp. 184-198
Author(s):  
Rajkumar Rajaseskaran ◽  
Mridual Bhasin ◽  
K. Govinda ◽  
Jolly Masih ◽  
Sruthi M.
Keyword(s):  
Health Services ◽  
Real Time ◽  
Patient Monitoring ◽  
The Body ◽  
Smart Device ◽  
Time Data ◽  
Remote Areas ◽  
Real Time Data

The objective is to build an IoT-based patient monitoring smart device. The device would monitor real-time data of patients and send it to the Cloud. It has become imperative to attend to minute internal changes in the body that affect overall health. The system would remotely take care of an individual's changes in health and notify the relatives or doctors of any abnormal changes. Cloud storages provide easy availability and monitoring of real-time data. The system uses microcontroller Arduino Nano and sensors – GY80, Heartbeat sensor, Flex sensor, and Galvanic Skin (GSR) sensor with a Wi-Fi Module.

Body Measurements Forecast Based on Factor Analysis and Radical Basis Function Net

2013 ◽  
Vol 328 ◽  
pp. 89-92
Author(s):  
Zheng Liu ◽  
Ji Tuo Li ◽  
Guang Chen ◽  
Guo Dong Lu
Keyword(s):  
Factor Analysis ◽  
Nonlinear Model ◽  
Basis Function ◽  
The Body ◽  
Whole Body ◽  
Body Measurements ◽  
Forecast Method ◽  
Model Based ◽  
Human Modeling ◽  
Clothing Manufacture

For acquiring the body measurements precisely and conveniently, this paper presents a forecast method with character parameters. The character parameters are chosen based on factor analysis. The nonlinear model based on radical basis function net builds the correlation between the character parameters and the detailed measurements. Through measuring a few character parameters easily we can obtain the whole body detailed sizes. This technology can be used in and benefit the clothing manufacture and human modeling.

IoT-Based Health Services Framework for Endless Ailment Administration at Remote Areas

2021 ◽  
pp. 412-428
Author(s):  
Rajkumar Rajaseskaran ◽  
Mridual Bhasin ◽  
K. Govinda ◽  
Jolly Masih ◽  
Sruthi M.
Keyword(s):  
Health Services ◽  
Real Time ◽  
Patient Monitoring ◽  
The Body ◽  
Smart Device ◽  
Time Data ◽  
Remote Areas ◽  
Real Time Data

The objective is to build an IoT-based patient monitoring smart device. The device would monitor real-time data of patients and send it to the Cloud. It has become imperative to attend to minute internal changes in the body that affect overall health. The system would remotely take care of an individual's changes in health and notify the relatives or doctors of any abnormal changes. Cloud storages provide easy availability and monitoring of real-time data. The system uses microcontroller Arduino Nano and sensors – GY80, Heartbeat sensor, Flex sensor, and Galvanic Skin (GSR) sensor with a Wi-Fi Module.

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