Nagoya University · Engineering
Professor Jacinto Colan's research lab specializes in advancing robot-assisted minimally invasive surgery, with a focus on enhancing surgical precision, dexterity, and accessibility. The lab develops innovative robotic systems and human-robot interfaces tailored for challenging surgical environments such as endonasal and laparoscopic procedures, emphasizing intuitive control and patient safety. Key research directions include inverse kinematics with remote center of motion constraints, open-source and low-cost robotic surgical tools via 3D printing, and the integration of tactile feedback technologies to restore natural tissue perception during surgery.
Figures are computed from collected data and may differ slightly.
The reduced workspace in endonasal endoscopic surgery (EES) hinders the execution of complex surgical tasks such as suturing. Typically, surgeons need to manipulate non-dexterous long surgical instruments with an endoscopic view that makes it difficult to estimate the distances and angles required for precise suturing motion. Recently, robot-assisted surgical systems have been used in laparoscopic surgery with promising results. Although robotic systems can provide enhanced dexterity, robot-assi
While kinaesthetic feedback has been extensively explored to restore the natural interaction between the surgeon and the surgical environment, tactile feedback remains largely confined to research settings. This is due to significant challenges in integrating tactile feedback into robotic systems and current limitations of sensing technologies.
Robot-assisted minimally invasive surgery (RMIS) has been shown to be effective in improving surgeon capabilities, providing magnified 3D vision, highly dexterous surgical tools, and intuitive human-robot interfaces for high-precision tool motion control. Robotic surgical tools (RST) are a critical component that defines the performance of an RMIS system. Current RSTs still represent a high cost, with few commercially available options, which limits general access and research on RMIS. We aim to
Minimally invasive surgery has undergone significant advancements in recent years, transforming various surgical procedures by minimizing patient trauma, postoperative pain, and recovery time. However, the use of robotic systems in minimally invasive surgery introduces significant challenges related to the control of the robot's motion and the accuracy of its movements. In particular, the inverse kinematics (IK) problem is critical for robot-assisted minimally invasive surgery (RMIS), where sati
Endoscopic endonasal surgery (EES) is a minimally invasive technique for removal of pituitary adenomas or cysts at the skull base. This approach can reduce the invasiveness and recovery time compared to traditional open surgery techniques. However, it represents challenges to surgeons because of the constrained workspace imposed by the nasal cavity and the lack of dexterity with conventional surgical instruments. While robotic surgical systems have been previously proposed for EES, issues concer
Laparoscopic surgery (LS) is a minimally invasive technique that offers many advantages over traditional open surgery: it reduces trauma, scarring, and shortens recovery time. However, an important limitation is the loss of tactile sensations. Although some progress has been made in robotic-assisted minimally invasive surgery (RMIS) setups, RMIS is still not widely accessible. This review aims to identify which tactile display technologies have been proposed and experimentally validated for the
This study evaluates the impact of step size selection on Jacobian-based inverse kinematics (IK) for robotic manipulators. Although traditional constant step size approaches offer simplicity, they often exhibit limitations in convergence speed and performance. To address these challenges, we propose and evaluate novel variable step size strategies. Our work explores three approaches: gradient-based dynamic selection, cyclic alternation, and random sampling techniques. We conducted extensive expe
In robot-assisted minimally invasive surgery (RMIS), inverse kinematics (IK) must satisfy a remote center of motion (RCM) constraint to prevent tissue damage at the incision point. However, most of existing IK methods do not account for the trade-offs between the RCM constraint and other objectives such as joint limits, task performance and manipulability optimization. This paper presents a novel method for manipulability maximization in constrained IK of surgical robots, which optimizes the rob
In this paper, we present a concept of a user interface for articulated forceps having increased number of degrees of freedom for minimally invasive transnasal endoscopic neurosurgery. Specifically, our aim is to develop a hands-on type user interface capable of intuitively controlling the position and orientation of the forceps and their multiple tip degrees of freedom simultaneously. We introduce a conceptual prototype of a user interface composed of a passive mechanism for holding a 4 degree-
Human-Robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature
Inverse kinematics (IK) plays an essential role in the field of robotics, enabling robots to determine the joint configurations required to achieve desired end-effector poses. Gradient-based numerical methods are commonly used for IK problem-solving, where the step size influences the convergence success and solving time. In this work, we propose a novel approach that employs dynamic step sizes based on the relative gradient norm at each iteration, aiming to improve solve rates and overall perfo
Recent advances in robotic learning in simulation have shown impressive results in accelerating learning complex manipulation skills. However, the sim-to-real gap, caused by discrepancies between simulation and reality, poses significant challenges for the effective deployment of autonomous surgical systems. We propose a novel approach utilizing image translation models to mitigate domain mismatches and facilitate efficient robot skill learning in a simulated environment. Our method involves the
Minimally invasive surgery (MIS) procedures benefit significantly from robotic systems due to their improved precision and dexterity. However, ensuring safety in these dynamic and cluttered environments is an ongoing challenge. This paper proposes a novel hierarchical framework for collision avoidance in MIS. This framework integrates multiple tasks, including maintaining the Remote Center of Motion (RCM) constraint, tracking desired tool poses, avoiding collisions, optimizing manipulability, an
Minimally Invasive Surgeries (MIS) are challenging for surgeons due to the limited field of view and constrained range of motion imposed by narrow access ports. These challenges can be addressed by robot-assisted endoscope systems which provide precise and stabilized positioning, as well as constrained and smooth motion control of the endoscope. In this work, we propose an online hierarchical optimization framework for visual servoing control of the endoscope in MIS. The framework prioritizes ma
This paper presents a force control based cooperative human-robot guidance system for transnasal endoscopic surgery. The aim is to accurately bring a long surgical instrument such as articulated forceps attached to a six degree-of-freedom robotic arm to the desired pre-insertion position. An appropriate orientation and position control is needed to ensure the safety of tool insertion through the narrow nasal cavity. This study explores an approach to providing the surgeon with highly accurate an
Open papers in the app to read, cite, and organize with AI.