Prototyping an AI-powered robotic picker for sorting shredded aluminum
Prototyping an AI-powered robotic picker for sorting shredded aluminum
批准号:
531437-2018
负责人:
Fennessy, Barbara
金额:
$1.82万
依托单位国家:
加拿大
项目类别:
Applied Research and Development Grants - Level 1
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31
中文摘要
eCycle Solutions在加拿大各地经营着多个资产回收和电子电气设备回收设施 **。eCycle Solutions是一家私人控股的加拿大公司,拥有400多名员工,日处理能力为300公吨 ** 吨,所有工厂的仓储和处理空间超过350,000平方英尺。传统上,在废弃电气和电子设备(WESTERN)回收设施中,过时的废弃电子产品 ** 被切碎以释放其材料。其目的是根据材料类型将这些碎片分离,以生产纯商品。为了实现这一点,使用了各种分选技术来分离这些材料,然而,由于这些技术效率低下,污染很普遍。为了解决这种污染问题,工人们手动从材料流中取出碎片。这个过程是劳动密集型的,** 耗时,单调,重复,不符合成本效益。** Conestogas为该项目提出的解决方案是开发一种拾取放置机器人作为机械拾取器,该机器人 ** 能够利用卷积神经网络(CNN)对切碎的废弃电子产品进行分类。CNN将“训练”机器人,使其模仿人类操作员的能力,并根据经验进行“学习”。这将提高纯度和加工速度,并将使大部分劳动力被部署到其他任务,这将导致降低加工成本,提高盈利能力。
英文摘要
eCycle Solutions operates multiple asset recovery and electronics and electrical equipment recycling facilities**across Canada. eCycle Solutions is privately held, Canadian owned with over 400 employees and 300 metric**tonnes of daily processing capacity, with over 350,000 sq/ft of warehousing and processing space across all sites.**Traditionally, at Waste Electrical and Electronic Equipment (WEEE) recycling facilities, outdated waste electronics**are shredded in order to liberate their materials. The objective is to then separate these shredded pieces based**on their material type to produce pure commodities. To accomplish this, a variety of sorting technologies are used**to separate these materials, however, contamination is prevalent due to inefficiencies in these technologies. To**address this contamination, workers manually remove pieces from the material stream. This process is labourintensive,**time-consuming, monotonous, repetitive, and not cost effective.**Conestogas proposed solution for this project is to develop a pick-n-place robot as a mechanical picker, which**is capable of sorting shredded waste electronics utilizing a Convolutional Neural Network (CNN). The CNN will**"train" the robot so that it will mimic the capability of the human operator and "learn" based on experience. This**will result in increased purity rates and processing speeds, and will enable much of the labour force to deployed to**other tasks, which will lead to reduced processing costs, and increased profitability.
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