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相关概念视频

Olfaction01:25

Olfaction

40.5K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
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Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

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The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
10.6K
Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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相关实验视频

Updated: May 5, 2026

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
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Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

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机器人气味来源定位通过视觉和嗅觉融合导航算法.

Sunzid Hassan1, Lingxiao Wang2, Khan Raqib Mahmud1

  • 1Department of Computer Science, Louisiana Tech University, 201 Mayfield Ave., Ruston, LA 71272, USA.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种新的融合导航算法,用于机器人气味源定位 (OSL). 它结合视觉和嗅觉,在复杂的环境中显著提高了搜索效率.

关键词:
基于计算机视觉的导航.灵感来自于的算法多模式机器人技术多模式气味来源的定位和定位机器人操作系统 机器人操作系统

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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

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Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
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相关实验视频

Last Updated: May 5, 2026

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Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

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Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect
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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 感官系统 感官系统

背景情况:

  • 机器人气味源定位 (OSL) 对在未知环境中的自主系统至关重要.
  • 传统的OSL依赖于仅嗅觉的策略,这些策略容易受到乱的空气流的影响.
  • 基于视觉的导航由于物理障碍而面临限制.

研究的目的:

  • 开发一种混合导航算法,将视觉和嗅觉融合在一起,以增强OSL.
  • 在动态和混乱的环境中解决单一感觉方法的局限性.
  • 为了提高机器人气味检测的效率和稳定性.

主要方法:

  • 为动态战略转移 (逆风,避障,视觉,嗅觉) 提出了一个层次控制机制.
  • 开发了一个定制的深度学习模型,用于视觉目标检测.
  • 实施了一种灵感来自的算法,用于基于嗅觉的导航.

主要成果:

  • 视觉和嗅觉融合算法在与仅视觉和仅嗅觉的方法相比显示出更高的性能.
  • 与仅视觉搜索相比,平均搜索时间减少了54%,与仅嗅觉搜索相比,平均搜索时间减少了30%.
  • 在一个充满障碍的环境中,在移动机器人上成功实现了实现.

结论:

  • 核聚变导航算法有效地克服了由动荡的空气流和物理障碍所带来的挑战.
  • 混合方法显著提高机器人气味源定位能力.
  • 这项技术对需要自主导航和环境传感的应用具有前景.