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Related Experiment Video

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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A Study on Multi-Robot Task Allocation in Railway Scenarios Based on the Improved NSGA-II Algorithm.

Yanni Shen¹, Jianjun Meng^1,2,3,4

¹School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study introduces a novel hybrid algorithm for railway maintenance robots to optimize task allocation, reducing costs and improving efficiency in smart railway systems. The new method enhances operational performance by minimizing completion time and travel distance.

Keywords:

MOPSO NSGA-II multi-objective optimization multi-robot systems railway multi-robot task allocation

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Area of Science:

Robotics and Automation
Industrial Engineering
Artificial Intelligence

Background:

Industry 4.0 drives smart industry development, yet railway maintenance robot task allocation lacks optimization for cost and efficiency.
Automated railway maintenance requires efficient multi-robot task allocation strategies to address operational challenges.

Purpose of the Study:

To develop an optimized multi-robot task allocation method for automated orbital bolt maintenance in smart railway systems.
To enhance operational efficiency by minimizing makespan and total travel distance for railway maintenance robots.

Main Methods:

An improved hybrid algorithm combining NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOPSO (Multi-Objective Particle Swarm Optimization) was proposed.
A dynamic task planning method using track bolt coordinates for environmental feature extraction and task area partitioning was developed.
A multi-elite archive strategy and adaptive crossover/mutation probabilities were integrated to prevent premature convergence and maintain solution diversity.

Main Results:

The proposed hybrid algorithm demonstrated superior performance in addressing the multi-robot task allocation problem for railway maintenance.
Experiments with varying task sizes and robot quantities validated the algorithm's effectiveness in minimizing makespan and travel distance.
The integration of MOPSO balanced local and global search, improving optimization efficiency and solution quality.

Conclusions:

The developed hybrid algorithm offers an effective solution for optimizing multi-robot task allocation in smart railway maintenance.
The approach enhances operational efficiency and cost-effectiveness in automated railway systems.
This research contributes to advancing unmanned technologies in smart railway infrastructure maintenance.