Medical Robotics [Working Title]

Hyper-redundant snakelike serial robots are of great interest due to their application in search and rescue during disaster relief in highly cluttered environments and recently in the field of medical robotics. A key feature of these robots is the presence of a large number of redundant actuated joints and the associated well-known challenge of motion planning. This problem is even more acute in the presence of obstacles. Obstacle avoidance for point bodies, nonredundant serial robots with a few links and joints, and wheeled mobile robots has been extensively studied, and several mature implementations are available. However, obstacle avoidance for hyper-redundant snakelike robots and other extended articulated bodies is less studied and is still evolving. This paper presents a novel optimization algorithm, derived using calculus of variation, for the motion planning of a hyper-redundant robot where the motion of one end (head) is an arbitrary desired path. The algorithm computes the motion of all the joints in the hyper-redundant robot in a way such that all its links avoid all obstacles present in the environment. The algorithm is purely geometric in nature, and it is shown that the motion in free space and in the vicinity of obstacles appears to be more natural. The paper presents the general theoretical development and numerical simulations results. It also presents validating results from experiments with a 12-degree-of-freedom (DOF) planar hyper-redundant robot moving in a known obstacle field.

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CORDIAL CYBORG

10.36106/ijsr/4234695 ◽

2021 ◽

pp. 29-31

Author(s):

Saumya Jaiswal ◽

Shivangi Tiwari ◽

Vivek Kumar Tripathi ◽

Ajay Sharma

Keyword(s):

Direct Contact ◽

Medical Robotics ◽

Medical Applications ◽

Collaborative Robotics ◽

Medical Robots ◽

The Past ◽

Medicine Prescription ◽

Hospital Stays ◽

Return Home ◽

Spread Of Infection

1. What are robots used in healthcare? Areas within healthcare which are starting to use robots include: telepresence, rehabilitation, medical transportation, sanitization and prescription dispensing. But we are most interested in collaborative robotics. We will be discussing the COBOT(Cordial Robot) applications. Most modern healthcare robots are especially designed for their target applications. 2. Is it possible to use robotics in medicine? Robotics in medicine can happen in many ways, here are some. Healthcare has been predicted as “a promising industry for robotics” for the past 45 years or more. Since as far back as 1974, researchers have been looking for ways to incorporate robotics into medical applications. 3. Is there a need for more surgery/telepresence/rehabilitation/medical transportation/sanitation and disinfection/medicine prescription dispensing robots? There is denitely a need for many more surgery robots, laparoscopic, endoscopic and nanorobots, as the technology allows more functionalities with miniature propulsion mechanisms. M.A. Zenati, M. Mahvash, from the science of medical robotics, 2012. 4. How are medical robots used to treat patients, reduce contact, and cure pain? Using the medical robots reduces the direct contact between the doctor and the patient, helps in reducing pain, by minimizing the need for more medication and longer hospital stays, allowing the person to return home by the therapy sooner without any spread of infection.

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The Pransky interview: Dr Nabil Simaan, Vanderbilt University Professor of Mechanical Engineering, Computer Science and Otolaryngology, Thought Leader in Medical Robotics

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-03-2021-0053 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Joanne Pransky

Keyword(s):

Computer Science ◽

Mechanical Engineering ◽

Single Port ◽

Industrial Robot ◽

Medical Robotics ◽

Surgical Robotics ◽

Robotic Systems ◽

Content Type ◽

Thought Leader ◽

Vanderbilt University

Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding his pioneering efforts. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Nabil Simaan, Professor of Mechanical Engineering, Computer Science and Otolaryngology at Vanderbilt University. He is also director of Vanderbilt’s Advanced Robotics and Mechanism Applications Research Laboratory. In this interview, Simaan shares his unique perspective and approaches on his journey of trying to solve real-world problems in the medical robotics area. Findings Simaan received his BSc, MSc and PhD in mechanical engineering from the Technion – Israel Institute of Technology. He served as Postdoctoral Research Scientist in Computer Science at Johns Hopkins University. In 2005, he joined Columbia University, New York, NY, as an Assistant Professor of Mechanical Engineering until 2010, when he joined Vanderbilt. His current applied research interests include synthesis of novel robotic systems for surgical assistance in confined spaces with applications to minimally invasive surgery of the throat, natural orifice surgery, cochlear implant surgery and dexterous bimanual microsurgery. Theoretical aspects of his research include robot design and kinematics. Originality/value Dr Simaan is a leading pioneer on designing robotic systems and mechanisms for medical applications. Examples include technologies for snake robots licensed to Intuitive Surgical; technologies for micro-surgery of the retina, which led to the formation of AURIS Surgical Robotics; the insertable robotic effector platform (IREP) single-port surgery robot that served as the research prototype behind the Titan Medical Inc. Sport (Single Port Orifice Robotic Technology). Simaan received the NSF Career award for young investigators to design new algorithms and robots for safe interaction with the anatomy. He has served as the Editor for IEEE International Conference on Robotics and Automation, Associate Editor for IEEE Transactions on Robotics, Editorial Board Member of Robotica, Area Chair for Robotics Science and Systems and corresponding Co-chair for the IEEE Technical Committee on Surgical Robotics. In January 2020, he was bestowed the award of Institute of Electrical and Electronics Engineers (IEEE) Fellow for Robotics Advancements. At the end of 2020, he was named a top voice in health-care robotics by technology discovery platform InsightMonk and market intelligence firm BIS Research. Simaan holds 15 patents. A producer of human capital, his education goal is to achieve the best possible outcome with every student he works with.

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Simultaneous Robot–World and Hand–Eye Calibration without a Calibration Object

Sensors ◽

10.3390/s18113949 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3949 ◽

Cited By ~ 3

Author(s):

Wei Li ◽

Mingli Dong ◽

Naiguang Lu ◽

Xiaoping Lou ◽

Peng Sun

Keyword(s):

Three Dimensional ◽

Real Data ◽

Medical Robotics ◽

Calibration Method ◽

Bundle Adjustment ◽

Data Sets ◽

Rigid Transformation ◽

Validation Experiment ◽

Dimensional Reconstruction ◽

Sparse Bundle Adjustment

An extended robot–world and hand–eye calibration method is proposed in this paper to evaluate the transformation relationship between the camera and robot device. This approach could be performed for mobile or medical robotics applications, where precise, expensive, or unsterile calibration objects, or enough movement space, cannot be made available at the work site. Firstly, a mathematical model is established to formulate the robot-gripper-to-camera rigid transformation and robot-base-to-world rigid transformation using the Kronecker product. Subsequently, a sparse bundle adjustment is introduced for the optimization of robot–world and hand–eye calibration, as well as reconstruction results. Finally, a validation experiment including two kinds of real data sets is designed to demonstrate the effectiveness and accuracy of the proposed approach. The translation relative error of rigid transformation is less than 8/10,000 by a Denso robot in a movement range of 1.3 m × 1.3 m × 1.2 m. The distance measurement mean error after three-dimensional reconstruction is 0.13 mm.

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