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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search.

Gregor Klančar¹, Andrej Zdešar¹, Mohan Krishnan²

¹Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

This study introduces a novel navigation function for robot path planning, effectively resolving local minima issues. The method generates smooth paths efficiently while adapting to dynamic environments using the A* algorithm.

Keywords:

bilinear interpolation dynamic local re-planning path planning potential field robot navigation

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Area of Science:

Robotics
Artificial Intelligence
Computer Science

Background:

The artificial potential field method is a common approach for robot path planning.
A significant limitation of this method is the occurrence of local minima, which can trap robots.
Existing extensions aim to overcome this weakness, but often with trade-offs in complexity or performance.

Purpose of the Study:

To propose a smooth navigation function that overcomes the local minima problem in robot path planning.
To develop a method that generates smooth paths with moderate computational complexity.
To ensure adaptability to dynamic environmental changes.

Main Methods:

A smooth navigation function is proposed, integrating Dijkstra-based discrete static potential field evaluation with modified bilinear interpolation.
The A* algorithm is employed to handle dynamic environmental changes by bypassing the static plan.
Modifications to bilinear interpolation are developed for path-planning applicability.

Main Results:

The proposed method effectively solves the local minima problem inherent in artificial potential fields.
Smooth paths are generated with a balance between computational complexity and preservation of the static plan.
The integration with the A* algorithm allows for efficient adaptation to dynamic environmental changes.

Conclusions:

The developed navigation model offers a robust solution for robot path planning, addressing critical limitations of existing methods.
It provides a computationally efficient approach to generating smooth, optimal paths in static and dynamic environments.
The strategy demonstrates significant advantages in various test environments, enhancing robot navigation capabilities.