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Related Concept Videos

Vision01:24

Vision

59.5K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Color Vision01:24

Color Vision

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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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.
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Manipulation and Analysis01:21

Manipulation and Analysis

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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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What is a Sensory System?01:31

What is a Sensory System?

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Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
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Predator-Prey Interactions02:39

Predator-Prey Interactions

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Predators consume prey for energy. Predators that acquire prey and prey that avoid predation both increase their chances of survival and reproduction (i.e., fitness). Routine predator-prey interactions elicit mutual adaptations that improve predator offenses, such as claws, teeth, and speed, as well as prey defenses, including crypsis, aposematism, and mimicry. Thus, predator-prey interactions resemble an evolutionary arms race.
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Related Experiment Video

Updated: Jan 26, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

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Vision for Robust Robot Manipulation.

Ester Martinez-Martin1, Angel P Del Pobil2,3

  • 1RoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain. ester@ua.es.

Sensors (Basel, Switzerland)
|April 10, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a vision-based method for robots to detect object loss during manipulation. Using depth cameras, robots can now reliably assess grip success and recover from drops in domestic settings.

Keywords:
depth visionrobot manipulationrobotics

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Domestic robots require robust object manipulation capabilities.
  • Existing proprioceptive sensors for grip detection can be costly and complex.
  • Vision-based sensing offers a rich alternative for robot manipulation tasks.

Purpose of the Study:

  • To develop a vision-based system for robustly evaluating object manipulation success in domestic robots.
  • To enable continuous monitoring of object presence and facilitate automatic recovery from manipulation failures.

Main Methods:

  • Utilizing depth cameras for enhanced environmental perception.
  • Employing Lab-colour segmentation to identify robot manipulators within images.
  • Leveraging depth information to detect contact edges between manipulators and objects.

Main Results:

  • The system accurately detects the presence or absence of contact points.
  • It enables continuous reporting of object loss during manipulation.
  • Experimental validation in realistic indoor environments confirms the approach's effectiveness.

Conclusions:

  • Depth-vision-based sensing provides a robust solution for evaluating robotic manipulation success.
  • This method enhances the reliability of domestic assistant robots by enabling object-loss recovery.
  • The approach offers a flexible and cost-effective alternative to traditional proprioceptive sensors.