Investigation of a single object shape for efficient learning in bin picking of multiple types of objects

IF 0.8 Q4 ROBOTICS
Isamu Bungo, Tomohiro Hayakawa, Toshiyuki Yasuda
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引用次数: 0

Abstract

In recent years, many studies have used Convolutional Neural Networks (CNN) as an approach to automate bin picking tasks by robots. In these previous studies, all types of objects in a bin were used as training data to optimize CNN parameters. Therefore, as the number of types of objects in a bin increases under the condition that the total number of training data is retained, CNN is not sufficiently optimized. In this study, we propose a learning method of CNN to achieve a bin picking task for multi-types of objects. Unlike previous learning method using multi-types of objects, we use a single type of object with a well-designed shape to obtain training data. It is true that when an object is used for training, then the learning for the corresponding object proceeds very well. However, the training does not contribute so much to the leaning of other types of objects. we expect the CNN to learn many types of grasping methods simultaneously by the training data of the single well-designed object. In that case, the total number of training data for each type of objects in a bin can be retained even if the number of types of objects increases. To verify the idea, we construct 12 different CNN models which are trained by different types of objects. Through simulations and robot experiments, bin picking tasks to pick multi-types of objects were performed using those CNN models. As a result, the training method which uses a complex-shaped object achieved higher grasping success rate than the training method which uses a primitive-shaped object. Moreover, the training method which uses a complex-shaped object achieved higher grasping success rate than the previous training method which uses all types of objects in a bin.

研究单一物体形状对多种类型物体分拣的高效学习作用
近年来,许多研究将卷积神经网络(CNN)作为机器人自动分拣垃圾的一种方法。在以往的研究中,垃圾箱中所有类型的物体都被用作优化 CNN 参数的训练数据。因此,在保留全部训练数据的条件下,随着垃圾箱中物体类型数量的增加,CNN 并没有得到充分优化。在本研究中,我们提出了一种 CNN 学习方法,以实现多类型物体的分仓任务。与以往使用多类型物体的学习方法不同,我们使用形状设计良好的单一类型物体来获取训练数据。诚然,当一个物体被用于训练时,相应物体的学习就会非常顺利。我们希望 CNN 能够通过单一设计良好的物体的训练数据同时学习多种抓取方法。在这种情况下,即使物体类型的数量增加,也可以保留一仓中每种类型物体的训练数据总数。为了验证这一想法,我们构建了 12 个不同的 CNN 模型,这些模型由不同类型的物体训练而成。通过模拟和机器人实验,我们使用这些 CNN 模型完成了多类型物品的拣选任务。结果,使用复杂形状物体的训练方法比使用原始形状物体的训练方法获得了更高的抓取成功率。此外,使用复杂形状物体的训练方法的抓取成功率也高于之前使用垃圾箱中所有类型物体的训练方法。
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来源期刊
CiteScore
2.00
自引率
22.20%
发文量
101
期刊介绍: Artificial Life and Robotics is an international journal publishing original technical papers and authoritative state-of-the-art reviews on the development of new technologies concerning artificial life and robotics, especially computer-based simulation and hardware for the twenty-first century. This journal covers a broad multidisciplinary field, including areas such as artificial brain research, artificial intelligence, artificial life, artificial living, artificial mind research, brain science, chaos, cognitive science, complexity, computer graphics, evolutionary computations, fuzzy control, genetic algorithms, innovative computations, intelligent control and modelling, micromachines, micro-robot world cup soccer tournament, mobile vehicles, neural networks, neurocomputers, neurocomputing technologies and applications, robotics, robus virtual engineering, and virtual reality. Hardware-oriented submissions are particularly welcome. Publishing body: International Symposium on Artificial Life and RoboticsEditor-in-Chiei: Hiroshi Tanaka Hatanaka R Apartment 101, Hatanaka 8-7A, Ooaza-Hatanaka, Oita city, Oita, Japan 870-0856 ©International Symposium on Artificial Life and Robotics
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