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Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot hTetro.

Balakrishnan Ramalingam1, Anh Vu Le2, Zhiping Lin3

  • 1ROAR Lab, Engineering Product Development, Singapore University of Technology and Design, Singapore, 487372, Singapore. balakrishnan@sutd.edu.sg.

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This study introduces smart floor cleaning robots that clean only dirty spots, not entire areas. This selective cleaning approach, using human traffic patterns and stain detection, improves robot efficiency and reduces accessory use.

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Floor cleaning robots are essential for public spaces but suffer performance degradation and increased accessory wear due to frequent, full-area cleaning.
  • Current cleaning robots operate inefficiently by cleaning entire areas regardless of localized soiling.

Purpose of the Study:

  • To develop a novel selective area cleaning framework for indoor floor cleaning robots.
  • To enhance robot efficiency by cleaning only identified dirty areas, reducing resource consumption.

Main Methods:

  • Utilized an RGB-D vision sensor network and deep learning for selective area identification.
  • Employed Simple Online and Real-time Tracking (SORT) for human traffic pattern analysis.
  • Integrated Single Shot Detector (SSD) MobileNet for detecting stains and trash.
  • Implemented an evolutionary-based optimization for efficient waypoint path planning to targeted cleaning zones.

Main Results:

  • The SSD MobileNet algorithm achieved 90% accuracy in detecting floor stains and trash.
  • The proposed evolutionary-based path planning reduced navigation time by 15% compared to conventional methods.
  • Energy consumption was reduced by 10% through optimized path planning.

Conclusions:

  • The selective area cleaning framework effectively identifies and targets dirty regions for robotic cleaning.
  • The integration of deep learning and optimized path planning significantly improves robot navigation efficiency and reduces operational costs.