优化工业仓库中基于无人机的库存检测和量化:激光雷达驱动的方法

Sotirios Tsakiridis, A. Papakonstantinou, Alexandros Kapandelis, Paris Mastorocostas, A. Tsimpiris, D. Varsamis
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引用次数: 0

摘要

技术的进步为工业运营带来了一场革命,专业工具在提高效率方面发挥着至关重要的作用。本研究深入探讨了物流部门在全球工业中的重要影响,并提出了一种利用配备激光雷达技术的无人飞行器(UAV)进行库存检测和识别的创新解决方案。现有的研究往往涉及复杂的硬件系统和算法,导致成本和计算需求增加,与此不同,我们的研究侧重于利用激光雷达数据和算法方法简化库存检测流程,从而最大限度地减少进入仓库对现有托盘进行量化的大量清点过程所需的时间。所提出的方法需要一架配备单光束高频激光雷达测距仪的定制四旋翼飞行器。无人机按照预定的飞行计划自主运行,捕捉仓库库存的高频测距数据。本文全面概述了无人机控制程序、利用激光雷达进行仓库扫描以及托盘库存检测和量化算法过程。所提出的方法以后处理方式处理激光雷达数据,估算托盘数量,从而生成仓库内每个堆垛的地图,标明托盘数量。研究结果表明,在模型仓库中成功实施了所建议的方法,评估准确率达到了令人印象深刻的 100%。未来的研究工作旨在将这一方法扩展到具有动态产品摆放的仓库,强调实时监控以进行全面的库存检测。对于寻求准确及时库存信息的行业来说,这种创新方法是一种经济高效的解决方案。
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Optimizing UAV-Based Inventory Detection and Quantification in Industrial Warehouses: A LiDAR-Driven Approach
The advancement of technology has brought about a revolution in industrial operations, where specialized tools play a crucial role in enhancing efficiency. This study delves into the significant impact of the logistics department in global industries and proposes an innovative solution for inventory detection and recognition using unmanned aerial vehicles (UAVs) equipped with LiDAR technology. Unlike existing research that often involves intricate hardware systems and algorithms leading to increased costs and computational demands, our research focuses on streamlining the inventory detection process by utilizing a LiDAR data and an algorithmic approach that minimizes the time of extensive counting process into the warehouse to quantify the pallets existing. The proposed methodology entails a custom-made quadcopter equipped with a single-beam and high-frequency LiDAR range finder. Operating autonomously along a predetermined flight plan, the drone captures high-frequency range data of warehouse inventory. The paper comprehensively outlines the UAV control procedures, warehouse scanning using LiDAR, and the inventory detection and quantification of pallets algorithmic process. The proposed method processes LiDAR data in a post-process way, estimating the number of pallets and, consequently, producing a map of each stack within the warehouse denoting the quantities of pallets. The research results showcase the successful implementation of the proposed approach in a model warehouse, achieving an impressive 100% evaluation accuracy. Future research endeavors aim to extend this methodology to warehouses with dynamic product placements, emphasizing real-time monitoring for comprehensive inventory detection. This innovative approach stands out as a cost-effective and efficient solution for industries seeking accurate and timely inventory information.
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