基于自适应深度卷积神经网络的抓取检测算法研究 | |
顾启鹏![]() | |
2021-06 | |
Pages | 96 |
Subtype | 硕士 |
Abstract | 抓取是机器人的一项重要技能,稳定可靠的抓取物体是机器人完成装配、搬运和分拣任务的基本要求。传统的机器人抓取方法需要根据物体几何属性,建立机器人手爪和物体之间的接触模型。在一些复杂场景中,由于很难获得物体的几何属性,因而需要机器人能够根据对场景的理解和推理,以及物体在场景中的功能性属性,自主建立抓取模式。近年来,基于深度学习的抓取检测方法成为机器人抓取领域的研究热点之一。大部分的自主抓取方法是基于大规模的数据学习,样本采集和标注的工作量大,降低了机器人自主学习的效率。迁移学习由于具备所需样本数据少以及学习能力快的优点,已成机器人自主抓取的重要研究方向。本文基于工业机器人应用场景,通过深度学习来研究机器人抓取检测的相关技术。论文的主要工作如下: |
Other Abstract | Grasping is an important skill of robots. Grasping objects stably and reliably is the basic requirement for robots to complete assembly, handling, and sorting tasks. The traditional robot grasping method needs to establish the contact model between the robot hand and the object according to the geometric properties of the object. In some complex scenes, it is difficult to obtain the geometric attributes of the object, so the robot needs to be able to independently establish the grasping mode according to the understanding and reasoning of the scene and the functional attributes of the object in the scene. In recent years, grasping detection method based on deep learning has become one of the research hotspots in the field of robot grasping. Most of the autonomous grasping methods are based on large-scale data learning, and the workload of sample collection and annotation is large, which reduces the efficiency of robot autonomous learning. Due to the advantages of fewer sample data and fast learning ability, transfer learning has become an important research direction of robot autonomous grasping. This thesis uses deep learning to study the related technology of robot grasp detection based on the industrial robot application scenarios. The main work of this paper is as follows: |
Keyword | 抓取检测 深度学习 功能性检测 领域自适应 |
Language | 中文 |
Sub direction classification | 机器学习 |
Document Type | 学位论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/44948 |
Collection | 复杂系统管理与控制国家重点实验室_机器人理论与应用 |
Recommended Citation GB/T 7714 | 顾启鹏. 基于自适应深度卷积神经网络的抓取检测算法研究[D]. 北京. 中国科学院自动化研究所,2021. |
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基于自适应深度卷积神经网络的抓取检测算法(9182KB) | 学位论文 | 开放获取 | CC BY-NC-SA |
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