Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control

Raul Fernando Garcia Azcarate, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala () and Mohan Rajesh Elara
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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

Mathematics, 2025, vol. 13, issue 14, 1-18

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.

Keywords: adaptive cleaning; terrain classification; deep learning; fuzzy logic; LiDAR-based perception (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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