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.

Robot-aided outdoor cleaning is a challenging task with plenty of room for improvement. The cleaning profile of a robot should be adjusted based on the availability of litter to ensure proper and efficient cleaning. This article proposes a novel system that integrates a Glasius Bio-Inspired Neural Network (GBNN) for coverage path planning with a vision-based cleaning profile adaptation scheme. The vision-based adaptation occurs based on the presence of leaves on the ground. The proposed system operates in two phases: an initial phase with low-power cleaning to cover areas with no leaves, while localizing leaf spots that require high-power cleaning, and a secondary phase focusing on cleaning these detected leaf spots using a high-power cleaning profile. This cleaning profile adaptation enables more efficient and effective cleaning. Simulation results showed a 47% improvement in energy efficiency for the proposed method compared to a method with no cleaning profile adaptation. Robot hardware tests conducted using the Panthera 2.0 outdoor cleaning robot have demonstrated the real-world applicability of the proposed method.