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A potential field model using generalized sigmoid functions.

Jing Ren, Kenneth A McIsaac, Rajni V Patel

    IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
    |April 10, 2007
    PubMed
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    This study introduces a generalized sigmoid function for artificial potential field (APF) models, enabling accurate object representation for arbitrary shapes. This enhances path planning and haptic feedback by preventing obstacle collisions.

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Computational Geometry

    Background:

    • The artificial potential field (APF) method is limited by the difficulty in accurately modeling objects with arbitrary shapes.
    • Existing potential field models struggle to represent complex geometries, hindering practical applications.

    Purpose of the Study:

    • To propose a novel potential function based on generalized sigmoid functions for accurate object representation.
    • To demonstrate the model's utility in path-planning and haptic feedback applications.

    Main Methods:

    • Developed a potential function using generalized sigmoid functions.
    • Constructed the model from implicit primitives or sampled surface data for 2D and 3D objects.
    • Applied the model to mobile robot path-planning and haptic feedback tasks.

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    Main Results:

    • The generalized sigmoid model provides accurate analytic descriptions of arbitrary object shapes.
    • The model requires modest computational resources during runtime.
    • Validation confirmed effective obstacle avoidance in path-planning and successful task completion in haptic feedback.

    Conclusions:

    • The generalized sigmoid potential field model overcomes limitations in representing arbitrary shapes.
    • This model offers an efficient and accurate solution for robotics and haptic applications.
    • The approach facilitates safe navigation and task execution by preventing obstacle penetration.