Real-time analysis of mobile machines using sparse matrix technique

The use of modern multibody simulation techniques enables the description of complex products, such as mobile machinery, with a high level of detail while still solving the equations of motion in real time. Using the appropriate modelling and implementation techniques, the accuracy of real-time simulation can be improved considerably. Conventionally, in multibody system dynamics, equations of motion are implemented using the full matrices approach that does not consider the sparsity feature of matrices. With this implementation approach, numerical efficiency decreases when sparsity increases. In this study, a numerical procedure based on semi-recursive and augmented Lagrangian methods for real-time dynamic simulation is introduced. To enhance computing efficiency, an equation of motion is implemented by employing the sparse matrix technique.

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A Recursive Formulation for Real-Time Dynamic Simulation of Mechanical Systems

Journal of Mechanical Design ◽

10.1115/1.2912764 ◽

1991 ◽

Vol 113 (2) ◽

pp. 158-166 ◽

Cited By ~ 19

Author(s):

Dae-Sung Bae ◽

Ruoh-Shih Hwang ◽

Edward J. Haug

Keyword(s):

Real Time ◽

Dynamic Simulation ◽

Recursive Algorithm ◽

Equations Of Motion ◽

Mechanical Systems ◽

Time Dynamic ◽

Time Simulation ◽

Kinematic Loops ◽

Multi Body ◽

Recursive Equations

A new recursive algorithm for real-time dynamic simulation of mechanical systems with closed kinematic loops is presented. State vector kinematic relations that represent translational and rotational motion are defined to simplify the formulation and to relieve computational burden. Recursive equations of motion are first derived for a single loop multi-body system. Faster than real-time performance is demonstrated for a closed loop manipulator, using an Alliant FX/8 multiprocessor. The algorithm is extended to multi-loop, multi-body systems for parallel processing real-time simulation in companion papers [1, 2] where performance of the algorithm on a shared memory multi-processor is compared with that achieved with other dynamic simulation algorithms.

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Parallel Processing for Real-Time Dynamic System Simulation

14th Design Automation Conference ◽

10.1115/detc1988-0065 ◽

1988 ◽

Author(s):

R. S. Hwang ◽

D.-S. Bae ◽

E. J. Haug ◽

J. G. Kuhl

Keyword(s):

Parallel Processing ◽

Real Time ◽

Equations Of Motion ◽

Companion Paper ◽

Time Dynamic ◽

Closed Loop Systems ◽

Time Simulation ◽

Efficiency And Effectiveness ◽

Generalized Force ◽

Recursive Dynamics

Abstract A parallel processing algorithm based on the recursive dynamics formulation presented in a companion paper [1] is developed for multiprocessor implementation. Lagrange multipliers associated with cut-joint constraints for closed loop systems are eliminated systematically, resulting to a minimal set of equations of motion. Concurrent generation of the system inertia matrix and the generalized force vector is exploited. A new computational structure for dynamic analysis is proposed for real-time parallel processing. Real-time simulation of a vehicle is performed to illustrate efficiency and effectiveness of the algorithm, even for interactive man-in-the-loop simulation.

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A Recursive Formulation for Real-Time Dynamic Simulation

14th Design Automation Conference ◽

10.1115/detc1988-0064 ◽

1988 ◽

Author(s):

D.-S. Bae ◽

R. S. Hwang ◽

E. J. Haug

Keyword(s):

Real Time ◽

Dynamic Simulation ◽

Recursive Algorithm ◽

Equations Of Motion ◽

Design Tool ◽

Real Time Simulation ◽

Time Dynamic ◽

Time Simulation ◽

Design Variation ◽

Multi Body

Abstract A new recursive algorithm for real-time, interactive dynamic simulation, animated graphics, and design variation analysis is presented for mechanical systems with closed loops. State vector kinematic relations that represent translational and rotational motion are defined, to simplify the formulation and to relieve computational burden. Recursive equations of motion are first derived for a single loop multi-body system. Faster than real-time performance is demonstrated for a closed loop robot, using an Alliant FX/8 multiprocessor. The algorithm is extended to multi-loop, multi-body systems for parallel processing real-time simulation in companion papers [1,2]. Performance of the algorithm on a shared memory multi-processor is compared with that achieved with other dynamic simulation algorithms. A vehicle example is used to demonstrate efficiency of the algorithm for real-time simulation and graphics rendering in a network environment, for use as an interactive design tool.

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Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽

10.3390/s21124045 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4045

Author(s):

Alessandro Sassu ◽

Jose Francisco Saenz-Cogollo ◽

Maurizio Agelli

Keyword(s):

Deep Learning ◽

Real Time ◽

Video Data ◽

Video Analytics ◽

Web Based ◽

Real Time Analysis ◽

Open Source Framework ◽

Cluster Configuration ◽

Time Requirements ◽

High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

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