The research focuses on the development of a robust home service robot that is capable of doing multiple types of household chores. This work considers the problem of garment perception and folding procedure by a home service robot, focusing on the task of recognizing a piece of spread-out garment on a flat platform. The robotic platform setup is as general as possible to enable the robot to cater for multiple types of household chores. We propose a novel approach to understand the perception for a piece of garment using particle-based polygonal model and an algorithm to best-fit the model into the garment in an image directly rather than designing the template from a software. The simplest way to analyze the basic configuration of a piece of unknown garment is to spread out the garment on a flat platform. There must be contrast between the background and the garment color to enable proper recognition process. At the moment, the garment is aligned at a certain orientation for simplicity. We defined the particle-based polygonal model for three garment categories: towel, shirt, and trousers. Each category has its own model and parameters. We presume a garment consists of at least one main body and other supplementary parts, for example, collar or sleeve, if they exist. Hence, we consider towel consists of only one main body, whereas shirt and trousers consist of one main body and two supplementary parts. Centroid and contour for each part of garment are measured after the garment is discretized. By matching the particle-based model into its discretized contour and generate the combined model, the parameters for each category of garment can be estimated. Once the parameters of garment are obtained from the proposed model, the folding procedure is then determined. Practical garment folding using different types of garments were executed to evaluate the proposed approach. Based on the experimental results, the proposed particle-based polygonal models for the three types of garments have been successful, where the parameters can be estimated, which provides important decision on the folding algorithm. Using our approach, different types and sizes of garment can be robustly handled by a home service robot.
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