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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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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...
522

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Related Experiment Video

Updated: Nov 10, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection.

Ching-Chang Wong1, Li-Yu Yeh1, Chih-Cheng Liu1

  • 1Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a robot manipulation planning method using semantic segmentation and keypoint detection for precise object re-orientation. The system effectively detects and repositions randomly placed objects to a desired pose, enhancing robotic task automation.

Keywords:
3D keypoint detectionMask R-CNNobject re-orientationpick-and-placesemantic segmentation

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Robotic manipulation of randomly oriented objects presents a significant challenge.
  • Accurate object pose estimation and re-orientation are crucial for automated tasks.
  • Existing methods often require precise object models or struggle with variations in object placement.

Purpose of the Study:

  • To propose a novel manipulation planning method for robot manipulators to re-orient objects.
  • To enable robots to detect and adjust the pose of randomly placed objects to a specified configuration.
  • To develop a category-level manipulation strategy independent of specific 3D object models.

Main Methods:

  • A 3D keypoint detection system using an RGB-D camera to capture object pose and generate 3D keypoints.
  • Semantic segmentation employing Mask Region-Convolutional Neural Network (Mask R-CNN) and Conditional Random Fields (CRFs) for pixel-level object classification.
  • Manipulation planning utilizing spherical linear interpolation (Slerp) for generating robot movements based on current and desired object poses.

Main Results:

  • The system successfully generates 3D keypoints and suction points for object manipulation.
  • Semantic segmentation accurately identifies object parts, enabling pose determination.
  • The manipulation planning system effectively re-orients objects to the target pose using Slerp and image-based adjustments.
  • Experimental validation using a robot manipulator and vacuum suction cup confirmed the system's capability.

Conclusions:

  • The proposed method provides an effective approach for object re-orientation in robotic manipulation.
  • Category-level manipulation planning is achievable through semantic segmentation and keypoint detection.
  • The system demonstrates robustness in handling randomly placed objects and achieving desired poses.