Work

Fuzzy control algorithm of cleaning parameters of street sweeper based on road garbage volume grading

Year: 2025

Type: article

Abstract: The research examines the challenges city street sweepers face, which struggles to adapt cleaning settings based on varying road garbage volume, resulting in inefficient cleaning and high energy consumption. The study proposes a fuzzy control algorithm for adjusting the cleaning parameters of street sweepers based on road garbage volume grading. It starts by utilizing the YOLO (You Only Look Once) v5 deep learning model for target detection and garbage classification on road surfaces. The algorithm calculates road garbage volume grading coefficients based on the coverage and weight of different types of garbage. These coefficients, along with the street sweeper's traveling speed, are used as input variables to develop a fuzzy control model for optimizing operational parameters such as the street sweeper disc brush and fan speeds. This model aims to achieve adaptive control of cleaning parameters, thereby enhancing the intelligence of street sweepers. Experimental results show that the proposed algorithm can achieve a 29.77% energy saving under the same road garbage conditions compared to the conventional gear control mode.

Source: Scientific Reports

DOI: 10.1038/s41598-025-92771-6

Authors: Haiying Wang, Chenguang Wang, Yang Ao, Xiaofeng Zhang

Institution: Chang'an University

Language: English

Topic: Municipal Solid Waste Management

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,690