# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 1 Introduction  
Cleaning robots can autonomously perform cleaning tasks in indoor or outdoor environments. They typically possess functionalities such as environmental recognition, path planning, autonomous navigation, intelligent obstacle avoidance, and automatic cleaning. Commercial applications of cleaning robots are widespread and can be found in apartments, hotels, shopping malls, public spaces, and outdoor areas like roads. With the introduction of concepts like \"Industry 4.0\" and the development guidelines outlined in \"Made in China 2025\", cleaning robots are undergoing continuous transformation towards greater intelligence. Water cleaning robots are making significant strides in their development, to address complex application scenarios [1]. The term \"spent fuel pool\" refers to the storage pool where used nuclear fuel from a nuclear reactor is placed after removal. Its primary task is storing the used nuclear fuel safely while awaiting further processing [2; 3]. In practical operations, factors such as activities within the pool, mechanical equipment wear, and airborne dust can lead to the generation of foam on the surface of the water of the spent fuel pool. Cleaning this foam is necessary for the spent fuel storage process and has been a persistent technological challenge. With the rise of intelligent manufacturing, cleaning robots have gained widespread application due to their environmental perception, decision-making, and motion control capabilities. Adopting cleaning robots for debris removal has become a trend, replacingtraditional manual retrieval and fixed filtration circuits. However, conventional cleaning robots face challenges such as opaque motion data and significant accuracy deviations during operation, hindering their ability to meet cleaning requirements. Therefore, there is an urgent need to leverage new technologies and methods to facilitate the transformation of traditional cleaning robots towards intelligence. A digital twin is an intelligent approach that integrates new sensing technologies, data collection technologies, virtual reality, 3D visualization, and intelligent human-machine interaction, and incorporates the concept of digital governance and interdisciplinary knowledge. The concept of digital twin was first proposed by Professor [PERSON] in 2003. Its core idea is to create a virtual digital representation that corresponds to objects or processes in the real world using digital technology [4]. Subsequently, many scholars at home and abroad have actively engaged in the research and practice of digital twin technology, achieving significant results. [PERSON] and others contemplated the ten major issues of digital twins, introduced the concept of a five-dimensional model, and provided theoretical foundations and ideas for applying digital twins in product design, technological applications, and lifecycle maintenance [5; 6; 7]. [PERSON] and others systematically discussed the concept, structure, and operational mode of industrial digital twin systems by comparing different categories [8]. [PERSON] and others established a digital twin robot working platform aimed at eliminating glitches. [PERSON] and others used a digital twin to establish a twin-body model of a gearbox, simulated the operational states of gearboxes under different conditions, and achieved precise fault diagnosis for gearboxes [9]. [PERSON] affected the product production process using a digital twin, enabling real-time control and optimization of product production, and facilitating the transition from mass production to personalized customization [10]. [PERSON], [PERSON], and others proposed a real-time data-driven digital twin workshop system architecture and technical route. They achieved real-time visualization monitoring, health management, and intelligent workshop production status fault diagnosis by building a virtual simulation environment of the actual physical behavior and employing an event-responsive data management approach [11].