Author
Listed:
- Raul Fernando Garcia Azcarate
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
- Akhil Jayadeep
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
- Aung Kyaw Zin
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
- James Wei Shung Lee
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
- M. A. Viraj J. Muthugala
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
- Mohan Rajesh Elara
(Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Spingapore 487372, Singapore)
Abstract
Outdoor cleaning robots must operate reliably across diverse and unstructured surfaces, yet many existing systems lack the adaptability to handle terrain variability. This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. A 128-channel LiDAR sensor captures signal intensity images, which are processed by a ResNet-18 convolutional neural network to classify floor types as wood, smooth, or rough. Simultaneously, pitch angles from an onboard IMU detect terrain inclination. These inputs are transformed into fuzzy sets and evaluated using a Mamdani-type fuzzy inference system. The controller adjusts brush height, brush speed, and robot velocity through 81 rules derived from 48 structured cleaning experiments across varying terrain and slopes. Validation was conducted in low-light (night-time) conditions, leveraging LiDAR’s lighting-invariant capabilities. Field trials confirm that the robot responds effectively to environmental conditions, such as reducing speed on slopes or increasing brush pressure on rough surfaces. The integration of deep learning and fuzzy control enables safe, energy-efficient, and adaptive cleaning in complex outdoor environments. This work demonstrates the feasibility and real-world applicability for combining perception and inference-based control in terrain-adaptive robotic systems.
Suggested Citation
Raul Fernando Garcia Azcarate & Akhil Jayadeep & Aung Kyaw Zin & James Wei Shung Lee & M. A. Viraj J. Muthugala & Mohan Rajesh Elara, 2025.
"Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control,"
Mathematics, MDPI, vol. 13(14), pages 1-18, July.
Handle:
RePEc:gam:jmathe:v:13:y:2025:i:14:p:2245-:d:1699277
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