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Related Concept Videos

Parallel Processing01:20

Parallel Processing

220
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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Visual System01:26

Visual System

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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...
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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.
882

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Sensor-level computer vision with pixel processor arrays for agile robots.

Piotr Dudek1, Thomas Richardson2, Laurie Bose3

  • 1Department of Electrical Engineering and Electronics, The University of Manchester, Manchester, UK.

Science Robotics
|June 29, 2022
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Summary
This summary is machine-generated.

Parallel processor arrays (PPAs) offer low-latency vision processing for agile robots. This technology embeds processors in pixels, enabling efficient, high-performance machine vision systems for robotics.

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Area of Science:

  • Robotics
  • Computer Vision
  • Embedded Systems

Background:

  • Conventional computing hardware faces challenges in low-latency vision processing for agile autonomous robots due to power and space constraints.
  • Advances in semiconductor technology enable Parallel Processor Arrays (PPAs), integrating processors within each pixel of an image sensor.

Purpose of the Study:

  • To review the history of image sensing and processing hardware with a focus on in-pixel computing.
  • To outline the features of a state-of-the-art smart camera system utilizing a PPA device (SCAMP-5).
  • To demonstrate PPA functionalities in robotic applications.

Main Methods:

  • Review of in-pixel computing history and PPA technology.
  • Description of the SCAMP-5 smart camera system architecture.
  • Demonstration of PPA sensing functionalities in robotic applications.

Main Results:

  • PPAs enable tight integration of sensing, processing, and memory, offering a balance of high performance, low latency, low power, and cost-effectiveness.
  • The SCAMP-5 system showcases PPA capabilities for agile ground and aerial vehicles.
  • Demonstrated functionalities include high-speed odometry, target tracking, obstacle detection, and avoidance.

Conclusions:

  • PPAs represent a significant advancement for machine vision in robotics.
  • Future developments in PPA devices promise more agile, robust, adaptable, and lightweight robotic systems.
  • In-pixel computing is key to overcoming limitations in current robotic vision processing.