scholarly journals Coupled Dynamical System Based Hand-Arm Grasp Planning under Real-Time Perturbations

2011 ◽  
Author(s):  
Ashwini Shukla ◽  
Aude Billard
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Grasp Planning ◽  
Coupled Dynamical System

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.

scholarly journals Real-Time Grasp Planning for Multi-Fingered Hands by Finger Splitting

2018 ◽  
Author(s):  
Yongxiang Fan ◽  
Te Tang ◽  
Hsien-Chung Lin ◽  
Masayoshi Tomizuka
Keyword(s):  
Real Time ◽  
Grasp Planning

scholarly journals Dynamic Motion Planning for Autonomous Assistive Surgical Robots

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 957 ◽  
Author(s):  
Alessio Sozzi ◽  
Marcello Bonfè ◽  
Saverio Farsoni ◽  
Giacomo De Rossi ◽  
Riccardo Muradore
Keyword(s):  
Dynamical System ◽  
Fixed Point ◽  
Motion Planning ◽  
Real Time ◽  
Surgical Robots ◽  
Dynamic Changes ◽  
Dynamic Motion ◽  
Dynamic Motion Planning ◽  
Trajectory Generator ◽  
Mechanical Constraint

The paper addresses the problem of the generation of collision-free trajectories for a robotic manipulator, operating in a scenario in which obstacles may be moving at non-negligible velocities. In particular, the paper aims to present a trajectory generation solution that is fully executable in real-time and that can reactively adapt to both dynamic changes of the environment and fast reconfiguration of the robotic task. The proposed motion planner extends the method based on a dynamical system to cope with the peculiar kinematics of surgical robots for laparoscopic operations, the mechanical constraint being enforced by the fixed point of insertion into the abdomen of the patient the most challenging aspect. The paper includes a validation of the trajectory generator in both simulated and experimental scenarios.

scholarly journals Coupled dynamical system based arm–hand grasping model for learning fast adaptation strategies

2012 ◽  
Vol 60 (3) ◽  
pp. 424-440 ◽  
Author(s):  
Ashwini Shukla ◽  
Aude Billard
Keyword(s):  
Dynamical System ◽  
Adaptation Strategies ◽  
Coupled Dynamical System ◽  
Fast Adaptation

scholarly journals The synchronization of coupled stochastic systems driven by symmetric α-stable process and Brownian motion

Filomat ◽  
2018 ◽  
Vol 32 (6) ◽  
pp. 2219-2245
Author(s):  
Shahad Al-Azzawi ◽  
Jicheng Liu ◽  
Xianming Liu
Keyword(s):  
Dynamical System ◽  
Mathematical Model ◽  
Brownian Motion ◽  
Gaussian Noise ◽  
Stochastic Systems ◽  
Stable Process ◽  
Symmetric Stable Process ◽  
And Brownian Motion ◽  
Coupled Dynamical System ◽  
Non Gaussian

The synchronization of stochastic differential equations (SDEs) driven by symmetric ?-stable process and Brownian Motion is investigated in pathwise sense. This coupled dynamical system is a new mathematical model, where one of the systems is driven by Gaussian noise, another one is driven by non- Gaussian noise. In this paper, we prove that the synchronization still persists for this coupled dynamical system. Examples and simulations are given.

A Cascaded Deep Learning Framework for Real-time and Robust Grasp Planning*

2019 ◽  
Author(s):  
Hongxiang Yu ◽  
Qianen Lai ◽  
Yuwei Liang ◽  
Yue Wang ◽  
Rong Xiong
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Grasp Planning ◽  
Learning Framework

scholarly journals Causality Analysis: Identifying the Leading Element in a Coupled Dynamical System

PLoS ONE ◽  
2015 ◽  
Vol 10 (6) ◽  
pp. e0131226 ◽  
Author(s):  
Amir E. BozorgMagham ◽  
Safa Motesharrei ◽  
Stephen G. Penny ◽  
Eugenia Kalnay
Keyword(s):  
Dynamical System ◽  
Causality Analysis ◽  
Coupled Dynamical System
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