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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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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...
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Related Experiment Video

Updated: Jul 18, 2025

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
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Depth-Dependent Control in Vision-Sensor Space for Reconfigurable Parallel Manipulators.

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
Summary
This summary is machine-generated.

A new control approach for reconfigurable parallel robots enables precise target tracking without prior trajectory knowledge. This method enhances robot capabilities by overcoming limitations like workspace constraints and singularities.

Keywords:
camera-space manipulationparallel robotvision-based control

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Area of Science:

  • Robotics
  • Control Systems
  • Computer Vision

Background:

  • Reconfigurable parallel robots offer advantages over traditional designs but present complex control challenges.
  • Existing control methodologies are not readily applicable to reconfigurable parallel manipulators.
  • Tracking unknown target trajectories is a critical but difficult task in many robotic applications.

Purpose of the Study:

  • To design and implement a novel control approach for reconfigurable parallel robots.
  • To enable accurate target tracking in vision-sensor, 3D, and joint spaces for a reconfigurable delta-type robot.
  • To address the lack of general control methodologies for reconfigurable parallel robots.

Main Methods:

  • Development of a control approach for reconfigurable parallel robots.
  • Implementation of controls in vision-sensor, 3D, and joint spaces.
  • Extension of the Velocity Linear Camera Model-Camera Space Manipulation (VLCM-CSM) methodology for vision-sensor space control.
  • Experimental validation on a reconfigurable delta-type parallel robot.

Main Results:

  • Achieved an average positioning error of 0.6 mm for static targets.
  • Obtained tracking errors of 2.5 mm, 3.9 mm, and 11.5 mm for linear trajectories at varying speeds.
  • Maintained a control cycle time of 16 ms.
  • Demonstrated superior performance compared to non-reconfigurable robot control.

Conclusions:

  • The proposed control approach is effective for reconfigurable parallel robots.
  • The method successfully enables accurate target tracking of unknown trajectories.
  • This work advances control strategies for complex, reconfigurable robotic systems.