增强现实技术辅助报废汽车动力电池拆卸方法

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Machines Pub Date : 2023-11-22 DOI:10.3390/machines11121041
Jie Li, Bo Liu, Liangliang Duan, Jinsong Bao
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引用次数: 0

摘要

全球电动汽车行业的快速发展给报废动力电池的管理带来了巨大挑战。报废动力电池含有宝贵的资源,如锂、钴、镍和其他金属,可在各种应用中回收和再利用。现有的拆解流程依赖于人工操作,耗时、耗力且容易出错。本研究提出了一种智能增强现实(AR)辅助拆卸方法,旨在通过实时为操作员提供场景感知和视觉引导来提高拆卸效率。该方法首先采用基于深度学习的实例分割方法来处理拆卸场景的红绿蓝三色(RGB-D)数据。该分割方法将拆卸对象实例分割开来,并根据从实例掩模中获得的相应深度信息重建其点云表示。此外,为了估计场景中拆卸目标的姿态并评估其拆卸状态,还使用了一种迭代闭点算法,将分割的点云实例与实际拆卸物体对齐。然后利用获取的信息生成 AR 指令,从而减少拆卸过程中用户频繁交互的需要。为了验证 AR 辅助拆卸系统的可行性,对报废汽车动力电池进行了实验。结果表明,这种方法大大提高了拆卸效率,降低了拆卸错误的频率。因此,研究结果表明,所提出的方法是有效的,有望用于大规模工业回收和拆卸作业。
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An Augmented Reality-Assisted Disassembly Approach for End-of-Life Vehicle Power Batteries
The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.
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来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
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