scholarly journals Robot Intelligent Grasp of Unknown Objects Based on Multi-Sensor Information

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1595 ◽  
Author(s):  
Shan-Qian Ji ◽  
Ming-Bao Huang ◽  
Han-Pang Huang
Keyword(s):  
Real Time ◽  
Online Algorithm ◽  
Sensor Data ◽  
Control Action ◽  
Robot Hand ◽  
Robot Arm ◽  
Damping Properties ◽  
Different Types ◽  
Sensor Information ◽  
Grasp Quality

Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the grasp pose will be further checked for grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent real-time grasp system was achieved that is reliable enough to handle various objects with unknown weights, friction, and stiffness. The robots used in this paper are the NTU 21-DOF five-finger robot hand and the NTU 6-DOF robot arm, which are both constructed by our Lab.

Real-time path planning procedure for a whole-sensitive robot arm manipulator

Robotica ◽  
1992 ◽  
Vol 10 (4) ◽  
pp. 339-349 ◽  
Author(s):  
Edward Cheung ◽  
Vladimir Lumelsky
Keyword(s):  
Real Time ◽  
Target Position ◽  
Time Algorithm ◽  
Sensor Data ◽  
Planning System ◽  
Whole Body ◽  
Time Signal ◽  
Robot Arm ◽  
Contact Points ◽  
Planning Procedure

SUMMARYWe consider the problem of sensor-based motion planning for a three-dimensional robot arm manipulator operating among unknown obstacles. When every point of the robot body is subject to potential collision. The corresponding planning system must include these four basic components: sensor hardware; real-time signal/sensory data processing hardware/software; a local step planning subsystem that works at the basic sample rate of the arm; and finally, a subsystem for global planning. The arm sensor system developed at Yale University presents a proximity sensitive skin that covers the whole body of the arm and consists of an array of discrete active infrared sensors that detect obstacles by processing reflected light. The sensor data then undergoes low level processing via a step planning procedure, which converts sensor information into local normals at the contact points in the configuration space of the robot. This paper presents preliminary results on the fourth component, a real-time algorithm that realizes the upper, global level of planning. Based on the current collection of local normals, the algorithm generates preferable directions of motion around obstacles, so as to guarantee reaching the target position if it is reachable. Experimental results from testing the developed system are also discussed.

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

scholarly journals Digital Twin: Origin to Future

2021 ◽  
Vol 4 (2) ◽  
pp. 36
Author(s):  
Maulshree Singh ◽  
Evert Fuenmayor ◽  
Eoin Hinchy ◽  
Yuansong Qiao ◽  
Niall Murray ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Access ◽  
Physical Entity ◽  
Industrial Systems ◽  
Digital Twin ◽  
Planning Optimization ◽  
Different Types ◽  
Pros And Cons ◽  
Number Of Publications ◽  
Digital Shadow

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Modified Nanoclays/Straw Fillers as Functional Additives of Natural Rubber Biocomposites

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 799
Author(s):  
Justyna Miedzianowska ◽  
Marcin Masłowski ◽  
Przemysław Rybiński ◽  
Krzysztof Strzelec
Keyword(s):  
Natural Rubber ◽  
Raw Materials ◽  
Research Work ◽  
Damping Properties ◽  
Main Research ◽  
Functional Additives ◽  
Different Types ◽  
Elastomeric Materials ◽  
Research Goal ◽  
The Impact

Increasingly, raw materials of natural origin are used as fillers in polymer composites. Such biocomposites have satisfactory properties. To ensure above-average functional properties, modifications of biofillers with other materials are also used. The presented research work aimed to produce and characterize elastomeric materials with a straw-based filler and four different types of montmorillonite. The main research goal was to obtain improved functional parameters of vulcanizates based on natural rubber. A series of composites filled with straw and certain types of modified and unmodified nano-clays in various ratios and amounts were prepared. Then, they were subjected to a series of tests to assess the impact of the hybrids used on the final product. It has been shown that the addition of optimal amounts of biofillers can, inter alia, increase the tensile strength of the composite, improve damping properties, extend the burning time of the material and affect the course of vulcanization or cross-linking density.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Examining the contribution of near real-time data for rapid seismic loss assessment of structures

2021 ◽  
pp. 147592172199621
Author(s):  
Enrico Tubaldi ◽  
Ekin Ozer ◽  
John Douglas ◽  
Pierre Gehl
Keyword(s):  
Real Time ◽  
Damage Assessment ◽  
Free Field ◽  
Seismic Source ◽  
Seismic Damage ◽  
Added Value ◽  
Sensor Data ◽  
Sources Of Information ◽  
Bridge Model ◽  
Seismic Damage Assessment

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, global positioning system receivers and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources.

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