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Updated: May 31, 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

Genetic algorithm-based coverage path planning for autonomous aircraft cabin cleaning by reconfigurable robot.

Cong Hien Dinh¹, Chong Yong Qi², Huynh Van Van³

¹Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.

|May 28, 2026

Summary

This study introduces a novel two-stage approach for autonomous aircraft cabin cleaning using a reconfigurable robot. It optimizes cleaning paths to reduce time and energy, improving efficiency during critical aircraft turnaround operations.

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Operation of the Collaborative Composite Manufacturing (CCM) System

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Last Updated: May 31, 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

Operation of the Collaborative Composite Manufacturing (CCM) System

Operation of the Collaborative Composite Manufacturing (CCM) System

Published on: October 1, 2019

Area of Science:

Robotics and Automation
Aerospace Engineering
Operations Research

Background:

Aircraft cabin cleaning is time-sensitive, posing challenges for conventional robots due to confined spaces.
Current cleaning robots lack adaptability for irregular aircraft cabin layouts.

Purpose of the Study:

To develop an optimal Coverage Path Planning (CPP) framework for autonomous aircraft cabin cleaning.
To enhance efficiency and reduce energy consumption during aircraft turnaround operations.

Main Methods:

A two-stage CPP approach using a reconfigurable robot.
Stage 1: Full-size configuration for open areas (aisles, galleys), skipping difficult rows.
Stage 2: Genetic Algorithm (GA)-based Traveling Salesman Problem (TSP) for optimal sequence of skipped areas, including reconfiguration energy model.

Main Results:

The proposed approach significantly outperforms conventional CPP strategies in coverage time and energy usage.
Demonstrated feasibility of reconfigurable robots for autonomous aircraft cabin cleaning.
Optimized path planning considering coverage efficiency, energy consumption, and reconfiguration cost.

Conclusions:

The integrated framework effectively balances cleaning efficiency, energy usage, and reconfiguration costs.
Reconfigurable robotic systems offer a viable solution for real-world autonomous aircraft cabin cleaning.
The study validates a significant improvement over existing CPP methods for aircraft cabin cleaning.