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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
714
Parallel Processing01:20

Parallel Processing

179
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
179
Visual System01:26

Visual System

616
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
616
Controller Configurations01:22

Controller Configurations

118
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
118

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相关实验视频

Updated: Jul 18, 2025

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

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在可重新配置并行操纵器的视觉传感器空间中依赖深度的控制.

Arturo Franco-López1, Mauro Maya1, Alejandro González2

  • 1Facultad de Ingenieria, Universidad Autonoma de San Luis Potosi, San Luis Potosi 78290, Mexico.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

对于可重新配置的并行机器人而言,一种新的控制方法可以在没有先前的轨迹知识的情况下实现精确的目标跟踪. 这种方法通过克服工作空间约束和奇点等局限性来增强机器人的能力.

关键词:
摄像机空间操纵的操作平行机器人平行机器人平行机器人基于视觉的控制控制基于视觉的控制

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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相关实验视频

Last Updated: Jul 18, 2025

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

25.5K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 控制系统 控制系统
  • 计算机视觉 计算机视觉

背景情况:

  • 可重新配置的并行机器人比传统设计具有优势,但存在复杂的控制挑战.
  • 现有的控制方法不适用于可重新配置的并行操纵器.
  • 在许多机器人应用中,跟踪未知的目标轨迹是一个关键但困难的任务.

研究的目的:

  • 为可重新配置并行机器人设计和实施一种新的控制方法.
  • 为了使可重新配置的三角形类型机器人能够在视觉传感器,3D和关节空间中准确地跟踪目标.
  • 为了解决可重新配置并行机器人的一般控制方法的缺乏.

主要方法:

  • 开发可重新配置并行机器人的控制方法.
  • 在视觉传感器,3D和联合空间中实施控制.
  • 扩展速度线性摄像机模型-摄像机空间操纵 (VLCM-CSM) 方法论用于视觉传感器空间控制.
  • 在可重新配置的三角形类型平行机器人上进行实验验证.

主要成果:

  • 对于静态目标,实现了0.6mm的平均定位误差.
  • 在不同速度的线性轨迹上获得的2.5mm,3.9mm和11.5mm的跟踪误差.
  • 维持了16个月的对照周期时间.
  • 与非可重新配置的机器人控制相比,证明了更高的性能.

结论:

  • 拟议的控制方法对于可重新配置的并行机器人是有效的.
  • 该方法成功实现了未知的轨迹的准确目标跟踪.
  • 这项工作推进了复杂,可重新配置的机器人系统的控制策略.