Identification of Contact Dynamics Model Parameters From Constrained Robotic Operations

2005 ◽  
Vol 128 (2) ◽  
pp. 307-318 ◽  
Author(s):  
M. Weber ◽  
K. Patel ◽  
O. Ma ◽  
I. Sharf
Keyword(s):  
Identification Problem ◽  
Real Space ◽  
Contact Dynamics ◽  
Sensor Data ◽  
Model Parameters ◽  
End Effector ◽  
Surface Areas ◽  
Novel Strategy ◽  
Dynamics Simulations ◽  
Contact Tasks

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.

scholarly journals Self-Tuning Distributed Fusion Filter for Multi-Sensor Networked Systems with Unknown Packet Receiving Rates, Noise Variances, and Model Parameters

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4436 ◽  
Author(s):  
Wang ◽  
Sun
Keyword(s):  
Optimal Estimation ◽  
Original System ◽  
Identification Problem ◽  
Sensor Data ◽  
Model Parameters ◽  
Packet Dropouts ◽  
Estimation Algorithms ◽  
Self Tuning ◽  
Time Systems ◽  
Distributed Fusion

In this study, we researched the problem of self-tuning (ST) distributed fusion state estimation for multi-sensor networked stochastic linear discrete-time systems with unknown packet receiving rates, noise variances (NVs), and model parameters (MPs). Packet dropouts may occur when sensor data are sent to a local processor. A Bernoulli distributed stochastic variable is adopted to depict phenomena of packet dropouts. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown MPs for a new augmented system. The recursive extended least squares (RELS) algorithm is used to simultaneously identify packet receiving rates and MPs in the original system. Then, a correlation function method is used to identify unknown NVs. Further, a ST distributed fusion state filter is achieved by applying identified packet receiving rates, NVs, and MPs to the corresponding optimal estimation algorithms. It is strictly proven that ST algorithms converge to optimal algorithms under the condition that the identifiers for parameters are consistent. Two examples verify the effectiveness of the proposed algorithms.

Contact Parameter Estimation With a Space Robot Verification Facility

2005 ◽  
Author(s):  
Julie Agar ◽  
Inna Sharf ◽  
Christian Lange ◽  
Yves Gonthier
Keyword(s):  
Parameter Estimation ◽  
Single Point ◽  
Accurate Determination ◽  
Point Contact ◽  
Contact Dynamics ◽  
Dynamics Simulation ◽  
Sensor Data ◽  
Modeling Tools ◽  
Contact Parameter ◽  
Contact Tasks

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.

Real-Space X-Ray Pair Distribution Function Analysis and Molecular-Dynamics Modelling of Diflunisal Channel Hydrates

2020 ◽  
Author(s):  
Anuradha Pallipurath ◽  
Francesco Civati ◽  
Jonathan Skelton ◽  
Dean Keeble ◽  
Clare Crowley ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Distribution Function ◽  
Structural Dynamics ◽  
First Principles ◽  
Pair Distribution Function ◽  
Function Analysis ◽  
Real Space ◽  
X Ray ◽  
Dynamics Modelling ◽  
Dynamics Simulations

X-ray pair distribution function analysis is used with first-principles molecular dynamics simulations to study the co-operative H<sub>2</sub>O binding, structural dynamics and host-guest interactions in the channel hydrate of diflunisal.

scholarly journals Imitation learning-based framework for learning 6-D linear compliant motions

2021 ◽  
Author(s):  
Markku Suomalainen ◽  
Fares J. Abu-dakka ◽  
Ville Kyrki
Keyword(s):  
Contact Force ◽  
Free Space ◽  
Prior Information ◽  
Goal Area ◽  
Imitation Learning ◽  
Learning From Demonstration ◽  
End Effector ◽  
Localization Accuracy ◽  
Novel Method ◽  
Contact Tasks

AbstractWe present a novel method for learning from demonstration 6-D tasks that can be modeled as a sequence of linear motions and compliances. The focus of this paper is the learning of a single linear primitive, many of which can be sequenced to perform more complex tasks. The presented method learns from demonstrations how to take advantage of mechanical gradients in in-contact tasks, such as assembly, both for translations and rotations, without any prior information. The method assumes there exists a desired linear direction in 6-D which, if followed by the manipulator, leads the robot’s end-effector to the goal area shown in the demonstration, either in free space or by leveraging contact through compliance. First, demonstrations are gathered where the teacher explicitly shows the robot how the mechanical gradients can be used as guidance towards the goal. From the demonstrations, a set of directions is computed which would result in the observed motion at each timestep during a demonstration of a single primitive. By observing which direction is included in all these sets, we find a single desired direction which can reproduce the demonstrated motion. Finding the number of compliant axes and their directions in both rotation and translation is based on the assumption that in the presence of a desired direction of motion, all other observed motion is caused by the contact force of the environment, signalling the need for compliance. We evaluate the method on a KUKA LWR4+ robot with test setups imitating typical tasks where a human would use compliance to cope with positional uncertainty. Results show that the method can successfully learn and reproduce compliant motions by taking advantage of the geometry of the task, therefore reducing the need for localization accuracy.

scholarly journals Similarity Embedding Networks for Robust Human Activity Recognition

2021 ◽  
Vol 15 (6) ◽  
pp. 1-17
Author(s):  
Chenglin Li ◽  
Carrie Lu Tong ◽  
Di Niu ◽  
Bei Jiang ◽  
Xiao Zuo ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Real Space ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Activity Data ◽  
Extensive Evaluation ◽  
Sensor Signals ◽  
Public Datasets

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

An Ab-Initio Multicenter Tight-Binding Model for Molecular Dynamics Simulations

1988 ◽  
Vol 141 ◽  
Author(s):  
Otto F. Sankey ◽  
David J. Niklewski
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Local Density ◽  
Tight Binding ◽  
Real Space ◽  
Hamiltonian Matrix ◽  
Binding Model ◽  
Annealing Study ◽  
Total Energies ◽  
Dynamics Simulations

AbstractA new, approximate method has been developed for computing total energies and forces for a variety of applications including molecular dynamics simulations of covalent materials. The method is tight-binding-like and is based on the local density approximation within the pseudopotential scheme. Slightly excited pseudo-atomic-orbitals are used, and the tight-binding Hamiltonian matrix is obtained in real space. The method is used to find the total energies for five crystalline phases of Si and the Si 2 molecule. Excellent agreement is found with experiment. A molecular dynamics simulated annealing study has been performed on the Si 3 molecule to determine the ground state configuration.

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