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农业人机交互:系统综述。

Human-Robot Interaction in Agriculture: A Systematic Review.

机构信息

Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), Charilaou-Thermi Rd, 57001 Thessaloniki, Greece.

Department of Computer Science and Telecommunications, University of Thessaly, 35131 Lamia, Greece.

出版信息

Sensors (Basel). 2023 Jul 28;23(15):6776. doi: 10.3390/s23156776.

DOI:10.3390/s23156776
PMID:37571559
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422385/
Abstract

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human-robot interactive systems.

摘要

在追求优化农业实践的效率、灵活性和适应性的过程中,人机交互(HRI)在农业中应运而生。在信息和通信技术不断进步的推动下,这种方法旨在克服源自固有复杂农业环境的挑战。本文系统地回顾了学术文献,以捕捉这一充满希望的领域的当前进展和趋势,并确定未来的研究方向。可以推断出,人们对这个领域的兴趣越来越大,这个领域依赖于结合多个学科的观点来获得全面的理解。所选论文的主题主要是协同目标检测,而模拟是主要的方法。此外,用于 HRI 应用的主要作物是甜瓜、葡萄和草莓。最后,协作和合作是最受欢迎的交互模式,同时也研究了不同程度的自动化。在所有情况下,人类和机器人的协同作用在系统性能、工人的体力工作负荷和执行任务所需的时间方面都取得了最佳效果。然而,尽管取得了相关进展,但要建立可行、功能齐全和安全的人机交互系统,还有很长的路要走。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/46022ec86843/sensors-23-06776-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/bda9728b415f/sensors-23-06776-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/b34eab056d3b/sensors-23-06776-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/116161a3a54e/sensors-23-06776-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/694951f65e28/sensors-23-06776-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/3d3da8d7f55d/sensors-23-06776-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/46022ec86843/sensors-23-06776-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/bda9728b415f/sensors-23-06776-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/b34eab056d3b/sensors-23-06776-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/116161a3a54e/sensors-23-06776-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/694951f65e28/sensors-23-06776-g004a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/3d3da8d7f55d/sensors-23-06776-g005a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4d4/10422385/46022ec86843/sensors-23-06776-g006.jpg

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