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Related Experiment Video

Updated: May 9, 2026

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

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

Published on: October 14, 2017

Real environment obstacle circular edge expansion design robot path planning based on ant colony algorithm.

Feng Li^1,2,3, Maoya Yang², Seong-Nam Jo²

¹Department of Intelligent Manufacturing, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China.

|May 7, 2026

Summary

This summary is machine-generated.

This study presents a novel method for converting real-world environment images into standard grid maps, enabling intelligent path planning for mobile robots. This approach simplifies robot motion control in complex environments.

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Mobile robot motion control relies heavily on intelligent path planning.
Current grid map generation using sensors is time-consuming and labor-intensive.
Artificial intelligence algorithms combined with grid maps offer a solution for path planning.

Purpose of the Study:

To develop a method for transforming non-standard real-world environment maps into standard grid maps.
To overcome the limitations of sensor-based grid map generation.
To facilitate intelligent path planning and motion control for mobile robots in real environments.

Main Methods:

Processing non-standard real environment maps to standardize size and shape.
Representing obstacles as circles with expanded edges for safety distance.
Utilizing image processing techniques to convert real-world maps into grid maps.

Main Results:

Successfully transformed real environment photos into standard grid maps.
The generated grid maps are suitable for robot path planning.
Demonstrated the feasibility of robot path planning and motion control using the proposed method.

Conclusions:

The developed method effectively converts real-world maps into usable grid maps for robots.
This approach enhances the efficiency and applicability of mobile robot path planning.
Enables robots to perform path planning and motion control in diverse real-world settings.