# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 1 Introduction  
The smart factory describes a future state of fully connected and functioning manufacturing systems conducting all the required tasks, from customer orders to production, without the need for human involvement [1].  
Recently, due to the COVID-19 pandemic, factories have accelerated the transition to contactless services in the manufacturing industry and smart factory transformation by utilizing advanced ICT technologies such as AI, IoT, cloud, robots, and big data. Owing to the different characteristics of the manufacturing and information fields, there are still many technical problems to be solved to accelerate the path of smart factories [2].  
In the manufacturing environment of the future, the manufacturing trend is changing from mass production systems to personalized production systems [3, 4].  
Robots, which serve as advanced manufacturing equipment in smart factories, are important to solve problems such as the decrease in the labor force and specialized human resources due to aging, and the increase in the demand for human convenience. Robots are widely used, with high repeatability, in various industrial processes, such as transfer, picking, painting, welding, and assembly processes, to optimize factory productivity. However, in a flexible production environment, changes in the product lead to a need for frequent robot teaching. The operator manually performs the robot teaching, which requires considerable time and effort [5]. The role of machine vision is important for the intelligent and automated teaching tasks of robots without human intervention.  
This is because robot machine vision improves factory productivity in the manufacturing processes (defect finding, transfer, material handling, welding, assembly, etc.); it also enables factories to meet high levels of automation and high-quality manufacturingrequirements because it provides visual and judgment information (environmental recognition, information acquisition, autonomous behavior, etc.) that enables a robot to act similar to a human being.  
Computer vision technology is being used and developed in various industries, such as autonomous driving, medical care, manufacturing, and logistics [6]. However, in manufacturing factories with high variability, it is difficult to accurately detect object localization and information due to various environmental factors, such as noise around objects, light reflections, and illumination interference.  
To solve this problem, we propose a robust algorithm for object localization that can easily and quickly respond to changes in the external environment by improving a previous work [7].  
The remainder of this paper is organized as follows: In Section 2, we describe the research trends related to object detection for changes in the external environment in computer vision. Section 3 introduces the robot grasping scenario in the structure of a personalized manufacturing service. Section 4 proposes an automated object localization algorithm based on the HSV color space. In Section 5, we analyze the performance evaluation and test results. Finally, Section 6 presents the conclusions.