IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-09-17 DOI:10.1016/j.compag.2024.109444
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引用次数: 0

摘要

在农业环境中,某些生产环境的非结构化性质,以及生产任务的高度复杂性和固有风险,为实现全自动化和有效的现场机器控制带来了巨大挑战。利用人类智能和精确机器动作的远程控制技术可确保操作员的安全并提高生产率。最近,虚拟现实(VR)克服了单一视角的限制,提供了三维信息,在远程控制应用中大有可为,但大多数研究并没有把重点放在农业环境上。因此,为了弥补这一差距,本研究提出了一个专为精准农业设计的大规模数字测绘和沉浸式人机远程操作框架。本研究利用大疆无人飞行器(UAV)进行数据采集,并引入了一种基于特征点的新型视频分割方法。为适应复杂纹理的多变性,该方法提出了一种增强型运动结构(SfM)方法。它整合了开放式多视图几何(OpenMVG)框架和来自变换器的局部特征(LoFTR)。增强型 SfM 生成点云图,通过多视图立体(MVS)进一步处理,生成完整的地图模型。在控制方面,引入了一个利用 TCP/IP 进行农业机械 VR 控制和定位的闭环系统。该系统提供了一种完全基于视觉的沉浸式控制方法,使操作人员能够利用 VR 技术进行远程操作。实验结果表明,本研究中开发的数字地图重建算法具有卓越的细节重建能力,同时还增强了鲁棒性和便利性。与传统的基于视频流的远程操作相比,这种用户友好型远程控制方法也展示了其优势,为操作员提供了更全面、更身临其境的体验,以及更高水平的态势感知能力。
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Immersive human-machine teleoperation framework for precision agriculture: Integrating UAV-based digital mapping and virtual reality control

In agricultural settings, the unstructured nature of certain production environments, along with the high complexity and inherent risks of production tasks, poses significant challenges to achieving full automation and effective on-site machine control. Remote control technology, which leverages human intelligence and precise machine movements, ensures operator safety and boosts productivity. Recently, virtual reality (VR) has shown promise in remote control applications by overcoming single-view limitations and providing three-dimensional information, yet most studies have not focused on agricultural settings. Therefore, to bridge the gap, this study proposes a large-scale digital mapping and immersive human–machine teleoperation framework specifically designed for precision agriculture. In this research, a DJI unmanned aerial vehicle (UAV) was utilized for data collection, and a novel video segmentation approach based on feature points was introduced. To accommodate the variability of complex textures, this method proposes an enhanced Structure from Motion (SfM) approach. It integrates the open Multiple View Geometry (OpenMVG) framework with Local Features from Transformers (LoFTR). The enhanced SfM produces a point cloud map, which is further processed through Multi-View Stereo (MVS) to generate a complete map model. For control, a closed-loop system utilizing TCP/IP for VR control and positioning of agricultural machinery was introduced. This system offers a fully visual-based method for immersive control, allowing operators to utilize VR technology for remote operations. The experimental results demonstrate that the digital map reconstruction algorithm developed in this study offers superior detail reconstruction, along with enhanced robustness and convenience. The user-friendly remote control method also showcases its advantages over traditional video streaming-based remote operations, providing operators with a more comprehensive and immersive experience and a higher level of situational awareness.

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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
期刊最新文献
TrackPlant3D: 3D organ growth tracking framework for organ-level dynamic phenotyping Camouflaged cotton bollworm instance segmentation based on PVT and Mask R-CNN Path planning of manure-robot cleaners using grid-based reinforcement learning Immersive human-machine teleoperation framework for precision agriculture: Integrating UAV-based digital mapping and virtual reality control Improving soil moisture prediction with deep learning and machine learning models
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