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Updated: Jul 16, 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

Path planning of industrial robots based on the adaptive field cooperative sampling algorithm.

Yongbo Zhuang¹, Sha Luo¹, Qingdang Li¹

¹College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong Province, China.

Frontiers in Neurorobotics

|December 1, 2025

Summary

This study introduces an adaptive field co-sampling (AFCS) algorithm to enhance industrial robot path planning. AFCS improves efficiency and generalization, outperforming traditional algorithms in complex environments.

Keywords:

AFCs APF RRT industrial robot path planning

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Last Updated: Jul 16, 2026

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Area of Science:

Robotics
Artificial Intelligence
Algorithm Design

Background:

Industrial robot path planning faces challenges with efficiency and generalization.
Traditional algorithms like Rapidly-exploring Random Trees (RRT) have limitations in complex environments.

Purpose of the Study:

To propose an Adaptive Field Co-sampling (AFCS) algorithm for improved industrial robot path planning.
To enhance the efficiency, generalization ability, and stability of path planning algorithms.

Main Methods:

Developed an environment complexity function to leverage environmental information.
Introduced an optimal sampling strategy to improve RRT algorithm's efficiency and directionality.
Integrated an improved Artificial Potential Field (APF) algorithm into RRT for collaborative node determination.

Main Results:

AFCS demonstrated significant improvements in environmental adaptability, stability, and efficiency compared to RRT, RRT*, and tRRT.
Simulation experiments in varying complexity environments validated the algorithm's performance.
Practical verification on a ROKAE industrial robot confirmed the algorithm's applicability.

Conclusions:

The proposed AFCS algorithm effectively addresses the limitations of traditional path planning methods for industrial robots.
AFCS offers a robust and efficient solution for complex robotic path planning tasks.
The algorithm shows high potential for real-world industrial applications.