ArticleScientific reports2025
Fuzzy control algorithm of cleaning parameters of street sweeper based on road garbage volume grading.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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