## 1 Introduction  
First introduced into factories in the 1960s [1], robotic systems have gained a foothold in the manufacturing world, particularly in the automotive industry. More recently, collaborative robots created to work side by side with humans are transforming the world of traditional robotics and production facilities [2, 3]. In the medical sector, the introduction of robotics and autonomous technologies is contributing to face the increasing number of patients per health professional and maintaining the existing level of quality in health services. In this context, automation allows, among other possibilities, professionals to carry out easier and safer diagnostic activities, not necessarily implying the presence of, or direct contact between, a doctor and his patient [4, 5, 6, 7]. Auscultation has been an integral component of clinical examination since the 19 th century, and is a highly cost-effective screening method for the detection of abnormal clinical symptoms [8]. Auscultation will continue to be essential in modern times, as cardiopulmonary disease is one of the largest underlying and direct causes of death worldwide, with a major influence on the quality of life and healthcare expenditure [9]. Additionally, this is a viable diagnostic technique for patients with COVID-19, and can be used as a follow-up tool for non-critical patients [10, 11]. Remote auscultation is advantageous in terms of infection prevention, as conventional auscultation requires clinicians to make physical contact with patients [12; 13]. The main challenge in auscultation is that its effectiveness depends on the hearing abilities and expertise of physicians [14]. Electronic stethoscopes have recently emerged as interesting solutions for remote auscultation [15; 16; 17; 18]. They allow the visualization of heart and lung sonograms during auscultation, making it simpler to distinguish between various types of heart and lung sounds, as well as a computer-assisted diagnosis in coronary artery disease [19]. Artificial intelligence (AI) is being applied to this equipment to decrease the subjectivity of auscultation [20; 21]. Although technology has increased the effectiveness of auscultation, the practice still involves physicians, or healthcare personnel, placing portable stethoscopes on the patient's body, requiring their physical presence. Additionally, during auscultation, they must place the stethoscope with adequate contact forces against the patient's skin, i.e., to ensure patient safety and to remove extraneous noise. Given the shortage of healthcare professionals, particularly in rural areas, where medical resources are scarce [19; 22], the creation of a robotic platform that can perform autonomous auscultation with high accuracy and repeatability, without relying on physician intervention, would be useful. Through a literature search, it was only possible to find two studies already conducted in this area. In the work developed by [PERSON] et al. [23], the goal was to develop a robotic auscultation platform, able to estimate auscultation positions and safely place the stethoscope in the estimated position. The estimation method is based on registration with a light detection and ranging (LiDAR) sensor. To autonomously perform the auscultation, the robotic system recognizes the auscultation positions based on the shape of the whole body and places the stethoscope in the determined auscultation positions, considering the positional relationship between the body and the stethoscope. The work developed by [PERSON] et al. [24], demonstrates, to the best of our knowledge, the first robotic system capable of performing autonomous auscultation of the heart and lungs, based on the TRINA robot. The goal of the robot is to provide diagnostically useful sounds, with high quality, for a physician to hear, rather than to perform the diagnosis itself.