## 1 Introduction
This may involve segregating the trash into separate compartments within the robot or depositing it into specific containers designated for recycling. The robot may employ techniques such as suction, grabbing, or other mechanical means to handle and manage the trash objects efficiently. Overall, this research can contribute to better recycling efforts, reducing the burden on landfills, and promoting sustainable practices. As I mentioned before, indoor suctionable object classification and recycling refers to the process of identifying and categorizing objects in indoor environments that are suitable for suctioning or removal by a vacuum cleaner, and subsequently recycling or appropriately disposing of them. At present, this research suffers from several challenges [10]:  
Absence of a Comprehensive Dataset: The absence of a comprehensive and diverse collection of data poses a challenge in achieving accurate indoor suctionable object classification. It requires significant efforts to collect a wide range of ground-view perspective images captured by robot vacuum cleaners, which encompass various object types, lighting conditions, and cluttered environments [11]. Edge Computing: Due to the constraints on size, weight, and power consumption, the computational resources in robot vacuum cleaners are limited compared to traditional computing devices like computers or smartphones [4; 5]. Hence, vacuum cleaner manufacturers usually focus on optimizing these resources (for example, a small CPU, micro-controller, and memory) to strike a balance between performance, efficiency, and cost-effectiveness, enabling the robot vacuum cleaners to perform their primary cleaning tasks effectively. More advanced AI tasks, such as multi-class object recognition, can be offloaded to powerful cloud servers, where data are processed and then sent back to robot vacuum cleaners for execution. However, edge computing inside vacuum cleaners offers several benefits over cloud-based computing in robot vacuum cleaner applications, such as low latency, reduced dependence on internet connectivity, privacy and security, real-time decision making, and cost savings [12]. First, edge computing processes data locally on the robot vacuum cleaner itself, reducing the time taken to send data to the cloud server and receive back the results. This low latency is crucial for real-time tasks like object classification and obstacle avoidance, enabling the robot to respond quickly to changing environmental conditions. Second, robot vacuum cleaners equipped with edge computing capabilities can operate autonomously even when there is limited or no internet connectivity. This independence from cloud services ensures uninterrupted functionality, regardless of internet outages or network disruptions. Third, edge computing keeps sensitive data and processing tasks within the device itself, reducing the need to send data to external servers. This enhances data privacy and security, because there is less exposure to potential information leaks or unauthorized access [13]. Fourth, the local processing capability of edge computing enables robot vacuum cleaners to make critical decisions in real-time without relying on cloud services. This is especially important for tasks like immediate navigation adjustments or emergency responses to objects. Fifth, with edge computing, there is less reliance on expensive cloud-based service, resulting in potential cost savings of high-speed internet connections for users [14]. Limited Visual Features: Small suctionable objects often lack distinctive visual features that can aid in classification. Unlike larger objects with prominent characteristics, small objects may appear similar in color or shape, making it difficult for AI algorithms to differentiate between different types of small objects. Infrared (IR) sensors encounter difficulties when operating on dark flooring or carpets due to their inability to effectively process infrared signals.