Real-Time Hardware-in-the-Loop Simulation of a Hexaglide Type Parallel Manipulator on a Real Machine Controller

Ros, Javier; Yoldi, Roberto; Plaza, Aitor; Iriarte, Xabier

doi:10.1007/978-94-007-4902-3_62

Cited by 4 publications

(2 citation statements)

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“…These are frequently related to the presence of closed loops that limit the mobility of some bodies to a significant extent. In this study, the 6-PUS Hexaglide [41], shown in Fig. 8, is the selected low-mobility multibody system example (LME).…”

Section: Low-mobility Multibody Examplementioning

confidence: 99%

Simplification of multibody models by parameter reduction

Ros,

Iriarte,

Plaza

et al. 2017

Preprint

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Model selection methods are used in different scientific contexts to represent a characteristic data set in terms of a reduced number of parameters. Apparently, these methods have not found their way into the literature on multibody systems dynamics. Multibody models can be considered parametric models in terms of their dynamic parameters, and model selection techniques can then be used to express these models in terms of a reduced number of parameters. These parameter-reduced models are expected to have a smaller computational complexity than the original one and still preserve the desired level of accuracy. They are also known to be good candidates for parameter estimation purposes.In this work, simulations of the actual model are used to define a data set that is representative of the system's standard working conditions. A parameter-reduced model is chosen and its parameter values estimated so that they minimize the prediction error on these data. To that end, model selection heuristics and normalized error measures are proposed.Using this methodology, two multibody systems with very different characteristic mobility are analyzed. Highly considerable reductions in the number of parameters and computational cost are obtained without compromising the accuracy of the reduced model too much. As an additional result, a generalization of the base parameter concept to the context of parameter-reduced models is proposed.

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Section: Low-mobility Multibody Examplementioning

confidence: 99%

Simplification of multibody models by parameter reduction

Ros,

Iriarte,

Plaza

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…7,47 In addition to understanding the main components and data flow in a simulator, it is also important to highlight some implementation aspects that are motivated by application requirements and the fact that the operation of mobile robots typically involves concurrent processes, such as processing measurements and feedback signals from multiple sensors, commanding several motors, and carrying out high level planning and decision tasks. Applications involving hardware-in-the-loop for rapid prototyping of controllers, 42 and robotic applications relying on predictive control techniques that require accurate models of the system whose parameters must be identified online, while using the same self-built models to find adequate future motions, 54 must also consider other requirements such as task concurrency, coordination, and distributed execution of software components possibly over a network of host machines. 55 Hence, simulation tools can be designed to work as stand-alone applications, in which several functions are compiled into one executable linked against the simulator library, or designed to run as multi-process systems.…”

Section: A Simulators' Architecture and Dynamics Modeling Approachesmentioning

confidence: 99%

Survey and comparative study of free simulation software for mobile robots

2014

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SUMMARYIn robotics, simulation has become an essential tool for research, education, and design purposes. Various software tools for mobile robot simulation have been developed and have reached different levels of maturity in recent years. This paper presents a general survey of mobile robot simulation tools and discusses qualitative and quantitative aspects of selection of four major simulators publicly available at no cost: Carmen, Player-Stage-Gazebo, Open Dynamics Engine, and Microsoft Robotics Developer Studio. The comparison of the simulators is aimed at establishing the range of applications for which these are best suited as well as their accuracy for certain simulation tasks. The simulators chosen for detailed comparison were selected considering their level of maturity, modularity, and popularity among engineers and researchers. The qualitative comparison included a discussion of relevant features. The quantitative analysis entailed the development of a detailed dynamical model of a mobile robot on a road with varying slope. This model was used as benchmark to compare the accuracy of each simulator. The validity of the simulated results was also contrasted against measurements obtained from experiments with a real robot. This research and analysis should be very valuable to educators, engineers, and researchers who are always seeking adequate tools for simulating autonomous mobile robots.

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