Contact Parameter Estimation With a Space Robot Verification Facility

Author(s):  
Julie Agar ◽  
Inna Sharf ◽  
Christian Lange ◽  
Yves Gonthier

Computer simulations play an important role in the design and verification of space robotic operations since on-orbit tests are impossible to conduct before launch. Thus, accurate computer modeling and simulation of space robotic tasks is essential. Of particular difficulty are the space manipulator operations, that involve constrained or contact tasks. Here, the contact dynamics capability in the modeling tools becomes critical for high fidelity simulations. This in turn implies a need for accurate determination of contact parameters, which are used as inputs to contact dynamics simulation. In this work, the identification of contact dynamics parameters based on sensor data obtained during robotic contact tasks is considered. In particular, the contact parameter estimation problem is addressed for simple contacting geometries using the SPDM Task Verification Facility Manipulator Testbed (SMT) at the Canadian Space Agency, where SPDM is the Special Purpose Dexterous Manipulator. The SMT is a robotic simulation facility, which also features gravity compensation algorithms to support the emulation of space robots. Single point SMT contact experiments were performed with six different payloads. Eight unique single point contact parameter estimation algorithms were used as part of the process of identifying payload stiffness from SMT experimental data.

2005 ◽  
Vol 128 (2) ◽  
pp. 307-318 ◽  
Author(s):  
M. Weber ◽  
K. Patel ◽  
O. Ma ◽  
I. Sharf

With the fast advances in computing technology, contact dynamics simulations are playing a more important role in the design, verification, and operation support of space systems. The validity of computer simulation depends not only on the underlying mathematical models but also on the model parameters. This paper describes a novel strategy of identifying contact dynamics parameters based on the sensor data collected from a robot performing contact tasks. Unlike existing identification algorithms, this methodology is applicable to complex contact geometries where contact between mating objects occurs at multiple surface areas in a time-variant fashion. At the same time, the procedure requires only measurements of end-effector forces/moments and the kinematics information for the end-effector and the environment. Similarly to other methods, the solution is formulated as a linear identification problem, which can be solved with standard numerical techniques for overdetermined systems. Efficacy, precision, and sensitivity of the identification methodology are investigated in simulation with two examples: A cube sliding in a wedge and a payload/fixture combination modeled after a real space-manipulator task.


2002 ◽  
Vol 124 (4) ◽  
pp. 529-538 ◽  
Author(s):  
I. Sharf ◽  
G. Gilardi ◽  
C. Crawford

Correct modeling of friction forces during constrained robotic operations is critical to high-fidelity contact dynamics simulation. Such simulations are particularly important for the development, mission planning and operations analysis of space robotic systems. Most existing friction models employ the coefficient of friction to capture the relationship between the friction force and the normal load. Hence, accurate identification of this parameter is prerequisite to accurate simulation. This issue is particularly important for space robotic operations since friction characteristics of materials are very different in space. In this manuscript, the problem of identification of the coefficient of friction is investigated experimentally and numerically. The motivating application being space manipulator systems, our principal objective is to develop a practical off-line identification algorithm, requiring minimum number of measurements from sensors available on space robots. To this end, a strategy is proposed to determine the coefficient of friction by using only the measured end-effector forces. The key idea behind the method is that during one-point contact, these forces represent the contact force and hence, can be directly used to calculate the coefficient of friction. The proposed approach is tested with the experimental data from peg insertion experiments conducted on a planar robotics test-bed with a specially designed contact interface. The algorithm is generalized to arbitrary complex geometries and applied to identify the coefficient of friction for a simulated battery drop test.


2020 ◽  
Vol 11 ◽  
pp. 680-687
Author(s):  
Atasi Chatterjee ◽  
Christoph Tegenkamp ◽  
Herbert Pfnür

Even though there have been many experimental attempts and theoretical approaches to understand the process of electromigration (EM), it has not been quantitatively understood for ultrathin structures and at grain boundaries. Nevertheless, we showed recently that it can be used reliably for the formation of single atomic point contacts after careful pre-structuring of the initial Ag nanostructures. The process of formation of nanocontacts by EM down to a single-atom point contact was investigated for ultrathin (5 nm) Ag structures at 100 K by measuring the conductance as a function of the time during EM. In this paper, we compare the process of thinning by EM of structures with constrictions below the average grain size of Ag layers (15 nm) with that of structures with much larger initial constrictions of around 150 nm having multiple grains at the centre constriction prior to the formation of a point contact. Even though clear morphological differences exist between both types of structures, quantized conductance plateaus showing the formation of single point contacts have been observed for both. Here we put emphasis on the thinning process by EM, just before a point contact is formed. To understand this thinning process, the semi-classical regime before the contact reaches the quantum regime was analyzed in detail. For this purpose, we used experimental conductance histograms in the range between 2G 0 and 15G 0 and their corresponding Fourier transforms (FTs). The FT analysis of the conductance histograms exhibits a clear preference for thinning along the [100] direction. Using well-established models, both atom-by-atom steps and ranges of stability, presumably caused by electronic shell effects, can be discriminated. Although the directional motion of atoms during EM leads to specific properties such as the instabilities mentioned, similarities to mechanically opened contacts with respect to cross-sectional stability were found.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4029 ◽  
Author(s):  
Jiaxuan Wu ◽  
Yunfei Feng ◽  
Peng Sun

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.


2018 ◽  
Vol 116 (10-11) ◽  
pp. 708-722 ◽  
Author(s):  
Cristina Curreli ◽  
Francesca Di Puccio ◽  
Lorenza Mattei

CrystEngComm ◽  
2022 ◽  
Author(s):  
Angelo Gavezzotti ◽  
Leonardo Lo Presti ◽  
Silvia Rizzato

The science of organic crystals and materials has seen in a few decades a spectacular improvement from months to minutes for an X-ray structure determination and from single-point lattice energy...


Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1554
Author(s):  
Dongming Chen ◽  
Panpan Du ◽  
Bo Fang ◽  
Dongqi Wang ◽  
Xinyu Huang

Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups’ centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability.


ChemistryOpen ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 854-863
Author(s):  
Elmer Ccopa Rivera ◽  
Rodney L. Summerscales ◽  
Padma P. Tadi Uppala ◽  
Hyun J. Kwon

2020 ◽  
Vol 61 (1) ◽  
pp. 111-118
Author(s):  
Zili Liu ◽  
Chenfei Song ◽  
Jiawei Li ◽  
Xinbin Hou ◽  
Li Wang ◽  
...  

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