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Related Concept Videos

Vision01:24

Vision

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.
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

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Related Experiment Video

Updated: Jun 26, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A Modular Vision System for Practical Object Detection on Resource-Constrained Humanoid Robots.

Meng Cheng Lau1, Nicolas Pottier1

  • 1School of Engineering and Computer Science, Laurentian University, Greater Sudbury, ON P3E 2C6, Canada.

Biomimetics (Basel, Switzerland)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a modular vision system for humanoid robots, enabling efficient object detection on resource-constrained platforms using YOLOv9. The system achieves reliable perception at 4-8 FPS, improving robustness in complex environments.

Keywords:
ROSYOLOv9embedded AIhumanoidroboticsobject detectionreal-time vision

Related Experiment Videos

Last Updated: Jun 26, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Deploying deep learning vision systems on humanoid robots is challenging due to limited onboard computation and legacy software.
  • Resource constraints hinder the integration of modern AI capabilities into robotic platforms.

Purpose of the Study:

  • To present a modular vision system for practical object detection on resource-constrained humanoid robots.
  • To enable compatibility between modern deep learning frameworks and ROS-based robotic systems.
  • To optimize perception for CPU-only inference.

Main Methods:

  • Developed a modular vision system based on the YOLOv9 framework.
  • Implemented a dual-environment design to decouple perception from robot control.
  • Trained task-specific lightweight models for CPU-only inference.
  • Evaluated the system on FIRA RoboWorld Cup (Hurocup) tasks: Marathon, Basketball, and Archery.

Main Results:

  • Achieved reliable object detection at 4-8 FPS on constrained hardware.
  • Demonstrated improved robustness compared to traditional geometric vision methods in dynamic environments.
  • Showcased practical, responsive task-level perception for robotic decision-making.

Conclusions:

  • The proposed modular vision system is feasible for deploying modern object detection on embedded humanoid platforms.
  • The system effectively balances accuracy, computational cost, and responsiveness.
  • This approach enhances robotic capabilities in complex, dynamic scenarios.