Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Schemas01:42

Schemas

12.5K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
12.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Right broca homologue mediates the pain-depression circuit: a case-control Functional Near-Infrared Spectroscopy (fNIRS) study on language network remodeling in chronic pain-depression comorbidity.

BMC psychiatry·2026
Same author

Research on Apple Surface Disease Detection Method Based on Improved YOLOv11s.

Foods (Basel, Switzerland)·2026
Same author

A lightweight and cross-scale attention network for geological hazard detection in rescue robotics.

Scientific reports·2026
Same author

The mediating role of anxiety and depressive symptoms on the relationship between physical limitations and cognitive impairment among older adults in China: differences based on religious perspective.

Frontiers in psychology·2026
Same author

Meta-analysis of valved conduits in right ventricular outflow tract reconstruction: comparison of homograft, bovine jugular vein, and EPTFE valved conduits.

International journal of surgery (London, England)·2026
Same author

Impact of Mildly Elevated Alanine Transaminase on In-Hospital Outcomes and Statin Intolerance in Elderly Patients With Acute Myocardial Infarction: A Retrospective Cohort Study.

Cardiology research and practice·2026

相关实验视频

Updated: Apr 6, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.1K

收获机器人的遮蔽回避:一个轻量级的积极感知模型

Tao Zhang1, Jiaxi Huang1, Jinxing Niu1

  • 1School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于采摘水果的机器人,使用轻量级YOLOv8n模型和主动感知来避免阻塞的方法. 该系统在复杂的果园环境中提高了水果检测和机器人臂效率.

关键词:
主动感知策略是一个积极感知策略.收获机器人收获机器人轻量级的YOLOv8nn可以使用.避免堵塞 避免堵塞

更多相关视频

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K
Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm
09:00

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

4.4K

相关实验视频

Last Updated: Apr 6, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.1K
Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K
Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm
09:00

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

4.4K

科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 计算机视觉 计算机视觉
  • 人工智能的人工智能

背景情况:

  • 由于复杂的果园的封闭,果机器人难以识别和定位果实.
  • 现有的方法缺乏实时性能和有效的阻塞处理.

研究的目的:

  • 为水果采摘机器人开发一种避免堵塞的方法.
  • 在混乱的环境中提高水果识别和定位准确度.
  • 提高农业机器人的运行效率.

主要方法:

  • 开发了一种轻量级的YOLOv8n模型,配备C2f-FasterBlock和SE注意力,用于实时检测水果.
  • 设计了一个端到端的主动感知模型 (ResNet50,多模式融合) 以避免阻塞.
  • 在现实场景中使用机器人探索数据集训练模型.

主要成果:

  • YOLOv8n模型实现了0.885 mAP,83 FPS,并减少了模型大小 (4.3 MB).
  • 活动感知系统引导机器人手臂尽量减少遮,提高目标识别成功率.
  • 综合系统在模拟复杂环境中证明了更高的运营效率.

结论:

  • 拟议的方法有效地解决了水果收获机器人中阻塞的挑战.
  • 这种方法为开发用于复杂环境的强大的农业机器人提供了可行的途径.
  • 建议在各种现实条件下进行进一步验证和系统优化.