Computational efficiency of multi-body systems dynamic models

Antonya, Csaba; Boboc, Răzvan Gabriel

doi:10.1017/s0263574721000345

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…It has also been widely deployed in a number of domains, such as biotechnology, chemical industry, finance, and so on. Recently, there have been many research hotspots in these fields, such as research which analyzes computational efficiency of multi-body systems [2] and personnel task allocation [3] based on intelligent decision making. After modeling the physical world by Simulink, engineers need to check the model to find possible safety or security problems.…”

Section: Introductionmentioning

confidence: 99%

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Yang

2023

Electronics

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Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works.

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Section: Introductionmentioning

confidence: 99%

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Yang

2023

Electronics

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Multibody dynamics in robotics with focus on contact events

da Silva,

Coelho,

Gonçalves

et al. 2024

Robotica

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Multibody dynamics methodologies have been fundamental tools utilized to model and simulate robotic systems that experience contact conditions with the surrounding environment, such as in the case of feet and ground interactions. In addressing such problems, it is of paramount importance to accurately and efficiently handle the large body displacement associated with locomotion of robots, as well as the dynamic response related to contact-impact events. Thus, a generic computational approach, based on the Newton–Euler formulation, to represent the gross motion of robotic systems, is revisited in this work. The main kinematic and dynamic features, necessary to obtain the equations of motion, are discussed. A numerical procedure suitable to solve the equations of motion is also presented. The problem of modeling contacts in dynamical systems involves two main tasks, namely, the contact detection and the contact resolution, which take into account for the kinematics and dynamics of the contacting bodies, constituting the general framework for the process of modeling and simulating complex contact scenarios. In order to properly model the contact interactions, the contact kinematic properties are established based on the geometry of contacting bodies, which allow to perform the contact detection task. The contact dynamics is represented by continuous contact force models, both in terms of normal and tangential contact directions. Finally, the presented formulations are demonstrated by the application to several robotics systems that involve contact and impact events with surrounding environment. Special emphasis is put on the systems’ dynamic behavior, in terms of performance and stability.

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Computational efficiency of multi-body systems dynamic models

Cited by 2 publications

References 29 publications

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Improve Model Testing by Integrating Bounded Model Checking and Coverage Guided Fuzzing

Multibody dynamics in robotics with focus on contact events

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