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Related Concept Videos

Orthogonal Trajectories01:26

Orthogonal Trajectories

137
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
137
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...
817

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Related Experiment Video

Updated: Mar 18, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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Published on: August 22, 2025

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Trifocal Tensor-Based Adaptive Visual Trajectory Tracking Control of Mobile Robots.

Jian Chen, Bingxi Jia, Kaixiang Zhang

    IEEE Transactions on Cybernetics
    |July 9, 2016
    PubMed
    Summary

    This study introduces a robust visual servoing method for mobile robots using a trifocal tensor approach. It enables accurate trajectory tracking even with imperfect camera installations and limited visual information.

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    Area of Science:

    • Robotics
    • Computer Vision
    • Control Systems

    Background:

    • Mobile robot navigation requires precise visual feedback for trajectory tracking.
    • Existing methods often struggle with uncalibrated cameras and limited visual data.
    • Trifocal tensor offers a powerful tool for geometric reconstruction in computer vision.

    Purpose of the Study:

    • To develop a visual servoing system for nonholonomic mobile robots using a trifocal tensor.
    • To enhance trajectory tracking accuracy with a roughly installed monocular camera.
    • To extend the operational workspace and robustness of visual servoing systems.

    Main Methods:

    • A trifocal tensor-based approach is proposed for visual trajectory tracking.
    • A key frame strategy is introduced to relax constraints on image information sharing.
    • An adaptive controller based on Lyapunov methods is developed to handle unknown depth and extrinsic parameters.

    Main Results:

    • The proposed method effectively tracks desired trajectories using visual feedback.
    • The key frame strategy expands the workspace of the visual servo system.
    • The adaptive controller ensures performance in practical scenarios, including pose regulation.

    Conclusions:

    • The trifocal tensor-based visual servoing approach provides a robust solution for mobile robot trajectory tracking.
    • The key frame strategy and adaptive control enhance the system's applicability and reliability.
    • Simulations confirm the effectiveness of the proposed approach in diverse conditions.