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首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Robotic eye-to-hand coordination: Implementing visual perception to object manipulation
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Robotic eye-to-hand coordination: Implementing visual perception to object manipulation

机译：机器人的人与人之间的协调：实现视觉感知以进行对象操纵

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摘要

This paper integrates different novel intelligent concepts to perform scene analysis, hand-eye coordination and object manipulation to realize a concrete working robot named COERSU. Firstly, a robust tuner is presented to optimize the early visual processing based on genetic algorithms (GA). Then, a few architectures of the adaptive neuro-fuzzy inference system (ANFIS), multi-layer perceptron (MLP) and the K-nearest neighborhood (KNN) classifiers are compared to perform scene analysis and object recognition. Following on, new methods of performing eye-to-hand visual servoing based on neuro-fuzzy approaches are detailed and compared with relative visual servoing, a new method developed by the authors. Theoretical model, mathematical framework and convergence criteria for our visual servoing techniques are also provided. The experiments show that the performance of the hybrid intelligent methods converge to relative visual servoing in terms of accuracy. However, in terms of speed, hybrid intelligent methods outperform relative visual servoing. Snapshots of the experimental results from COERSU in a table-top scenario to manipulate some soft objects (e.g. fruit/egg) are provided to validate the methods.
机译：本文集成了各种新颖的智能概念来执行场景分析，手眼协调和对象操纵，以实现名为COERSU的具体工作机器人。首先，提出了一种鲁棒的调谐器，用于基于遗传算法（GA）来优化早期视觉处理。然后，比较了自适应神经模糊推理系统（ANFIS），多层感知器（MLP）和K近邻（KNN）分类器的几种体系结构，以进行场景分析和对象识别。接下来，详细介绍了基于神经模糊方法执行眼对眼视觉伺服的新方法，并将其与作者开发的一种新方法相对视觉伺服进行了比较。还提供了我们视觉伺服技术的理论模型，数学框架和收敛准则。实验表明，在精度上，混合智能方法的性能收敛于相对视觉伺服。但是，就速度而言，混合智能方法的性能优于相对视觉伺服。提供了COERSU在桌面场景中操作一些软物体（例如水果/鸡蛋）的实验结果的快照以验证方法。

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