Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms.

Over the last years, minimally invasive surgery (MIS) has continuously improved due to new techniques and technologies. One of these novel techniques is hand-assisted laparoscopic surgery (HALS), where the surgeon inserts one hand through a small incision inside the abdominal cavity of the patient. As this kind of intervention only allows the use of one laparoscopic tool, the surgeon requires a deep collaboration with the assistant in order to coordinate their movements for performing a surgical maneuver. In this way, the replacement of a human assistant with a robot system specifically designed for HALS must include a natural human-machine interface and the capability for taking autonomous decisions. These features need the implementation of a model of the surgical protocol, a system capable of recognizing the corresponding surgical gestures made by the surgeon, and an autonomous task system that assists the surgeon without his direct intervention. This paper is focused on the design of a collaborative surgical robot that implements all the features previously described. This robot assistant has been validated by means of in vitro laparoscopic sutures on a HALS scenario with CISOBOT, a two-arm robotic platform designed and developed at the University of Malaga.

Endonasal surgery is a minimally invasive approach for the removal of pituitary tumors (sarcomas). In this type of procedure, the surgeon has to complete the surgical maneuvers for sarcoma resection with extreme precision, as there are many vital structures in this area. Therefore, the use of robots for this type of intervention could increase the success of the intervention by providing accurate movements. Research has focused on the development of teleoperated robots to handle a surgical instrument, including the use of virtual fixtures to delimit the working area. This paper aims to go a step further with a platform that includes a teleoperated robot and an autonomous robot dedicated to secondary tasks. In this way, the aim is to reduce the surgeon’s workload so that he can concentrate on his main task. Thus, the article focuses on the description and implementation of a navigator that coordinates both robots via a force/position control. Finally, both the navigation and control scheme were validated by in-vitro tests.

Minimally invasive surgery (MIS) techniques are growing in quantity and complexity to cover a wider range of interventions. More specifically, hand-assisted laparoscopic surgery (HALS) involves the use of one surgeon’s hand inside the patient whereas the other one manages a single laparoscopic tool. In this scenario, those surgical procedures performed with an additional tool require the aid of an assistant. Furthermore, in the case of a human–robot assistant pairing a fluid communication is mandatory. This human–machine interaction must combine both explicit orders and implicit information from the surgical gestures. In this context, this paper focuses on the development of a hand gesture recognition system for HALS. The recognition is based on a hidden Markov model (HMM) algorithm with an improved automated training step, which can also learn during the online surgical procedure by means of a reinforcement learning process.

This paper presents a cognitive architecture for a camera robotic assistant aimed at providing the proper camera view of the operating area in an autonomous way. The robotic system is composed of a miniature camera robot and an external robotic arm. The camera robot is introduced into the abdominal cavity and handled by the external robot through magnetic interaction. The cognitive architecture is provided with a long-term memory, which stores surgical knowledge, behaviors of the camera and learning mechanisms, and a short-term memory that recognizes the actual state of the task and triggers the corresponding camera behavior. To provide the proper camera view, each state of the task is characterized by a Focus of Attention (FOA), defined by an object, a position of the object in the image, and a zoom factor. The architecture also includes a learning mechanism to take into account particular preferences of surgeons concerning the viewpoint of the scene. The architecture proposed is validated through a set of in-vitro experiments.