# Sewer Cleaning Robot: A Visually Assisted Cleaning Robot for Sewers
###### Abstract  
Aiming to solve the problem of clearing obstacles in narrow and complex sewers, this paper introduces a visually assisted Sewer Cleaning Robot (SCR) for cleaning sewers with diameters ranging from 280 to 780 mm. The main work is carried out as follows: (a) A mobile platform is equipped with a pressing mechanism to press against the pipe walls in different diameters. The arm uses high-load linear actuator structures, enhancing load capacity while maintaining stability. (b) A Detection-Localization-Cleaning mode is proposed for cleaning obstacles. The YOLO detection model is used to identify six types of sewer defects. Target defects are then localized using monocular vision based on edge detection within defect bounding boxes. Finally, cutting is performed according to the localized defect positions. The feasibility of SCR in cleaning operations is validated through a series of experiments conducted under simulated pipeline conditions. These experiments evaluate its mobility, visual detection, and localization capabilities, as well as its ability to clear hard obstacles. This paper provides technical reserves for replacing human labor that use vision algorithms to assist in cleaning tasks within sewers. s +
Footnote †: journal: Applied sciences  
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defect recognition, allowing operators to assess pipeline conditions effectively. Although these robots excel at identifying problems, they are limited to inspection tasks and lack the ability to perform cleaning operations. This functional limitation necessitates additional tools or manual intervention for defect removal, increasing cleaning time and complexity. The modular tooling approach has demonstrated success in industrial robotics. For instance, [PERSON] [14] achieved multi-process manufacturing through interchangeable tools on a single robotic platform. However, implementing such systems in sewers faces unique challenges. Many cleaning robots have been developed to address specific operational needs within sewers. For example, some robots were equipped with cutting tools designed for removing tree roots [15; 16], while others focused on sediment removal using suction or scraping mechanisms [17; 18]. Tugeumwolachot's CIPbot-2 specializes in grinding operations, effectively cleaning hardened obstacles such as concrete deposits [19]. While these designs are highly effective in fulfilling their specialized cleaning tasks, the absence of visual systems limits their ability to provide real-time feedback on operational targets, making it challenging to accurately identify and locate obstacles such as roots, sediment, or debris during operation. Additionally, several commercially developed sewer robots are equipped with various cutting tools to clear foreign objects from pipelines, and they are fitted with rotatable cameras to provide real-time observation of operational targets [20; 21; 22]. [PERSON] designed an automated robot capable of rapidly cleaning obstacles [23]. [PERSON] introduced a robot equipped with an IR camera to relay real-time images of the sewage to a user-controlled mobile application. The user can then decide whether to intervene by directing the arm attached to the vehicle [24]. Due to the complexity of the sewer environment and the homogenization of images, these robots have difficulty performing cleaning tasks effectively when solely relying on manual operation. This paper proposes a visually assisted Sewer Cleaning Robot (SCR), designed for efficient sewer cleaning. The mobile platform is equipped with a pressing mechanism, which improves stability during cleaning operation and enhances ability to cross obstacles within the complex sewer environment. The robot arm uses a linear actuator to replace rotational motors in the two pitch joints for increasing the load capacity. A Detection-Localization-Cleaning mode is propoesd for optimizing the cleaning process. This mode integrates the YOLO defect detection model and a monocular vision localization algorithm to enhance obstacle cleaning efficiency.