Identification of the highest wrinkle grasping point of a folded hospital gown

There are already more than one billion people over the age of 60, and the World Health Organization predicts that number will increase to 1.4 billion by the year 2030. As a result, the need for caretakers is increasing, which could make society in the future unable to provide it. In this scenario, the need for automated assistance increases as the global population ages. One area of robotics where robots have demonstrated tremendous promise in closely collaborating with people is service robotics. Hospitals, residences, and facilities for the elderly will all require the deployment of intelligent robotic agents to carry out regular tasks. Cloth manipulation is one such daily activity and represents a challenging area for a robot. The research goal of this paper focused on finding the grasping points of the highest wrinkle (from a later point of view) of a folded hospital gown to then unfold it and help dressing a patient. The wrinkle is detected using the Generative Grasping Convolutional Neural Network (GG-CNN2), while the approach to the cloth by a manipulator is obtained by designing a visual servoing algorithm that considers the input of the GG-CNN2. In conclusion, the results described in this paper tend to study by deep some AI-based approaches for cloth manipulation capabilities; in particular, we concentrated on studying how to identify the first wrinkle of a cloth by combining the visual servoing approach with a neural network.