Automatic guidance of an assistant robot in laparoscopic surgery

Casals, Alı́cia; Amat, Josep; Laporte, Éric

doi:10.1109/robot.1996.503886

Cited by 69 publications

(41 citation statements)

References 2 publications

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“…For example, one four-bar mechanism could have one actuated DOF, while a second four-bar mechanism on top of it could be non-actuated (passive), thus the control of the first four-bar mechanism is sought which controls both stably. Furthermore, the term passive degree of freedom is sometimes used for underactuated manipulators and are accounted for structural flexibilities (Jain and Rodriguez, 1993;Casals and Amat, 1996). Another term one encounters in this setting is "redundant DOF" (Zhang et al, 2003), which however may lead to confusion as the term redundant DOF is used in robotics for robots featuring a greater number of actuators than necessary for a given task space which allows for special control movements (such as the human arm having seven relative joint motions for six DOFs at the hand).…”

Section: Types Of Isolated Degrees Of Freedommentioning

confidence: 99%

Solving the double-banana rigidity problem: a loop-based approach

2016

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Abstract. Rigidity detection is an important tool for structural synthesis of mechanisms, as it helps to unveil possible sources of inconsistency in Grübler's count of degrees of freedom (DOFs) and thus to generate consistent kinematical models of complex mechanisms. One case that has puzzled researchers over many decades is the famous "double-banana" problem, which is a representative counter-example of Laman's rigidity condition formula for which existing standard DOF counting formulas fail. The reason for this is the body-by-body and joint-by-joint decomposition of the interconnection structure in classical algorithms, which does not unveil structural isotropy groups for example when whole substructures rotate about an "implied hinge" according to Streinu. In this paper, a completely new approach for rigidity detection for cases as the "double-banana" counterexample in which bars are connected by spherical joints is presented. The novelty of the approach consists in regarding the structure not as a set of joint-connected bodies but as a set of interconnected loops. By tracking isolated DOFs such as those arising between pairs of spherical joints, rigidity/mobility subspaces can be easily identified and thus the "double-banana" paradox can be resolved. Although the paper focuses on the solution of the double-banana mechanism as a special case of paradox bar-and-joint frameworks, the procedure is valid for general body-and-joint mechanisms, as is shown by the decomposition of spherical joints into a series of revolute joints and their rigid-link interconnections.

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Section: Types Of Isolated Degrees Of Freedommentioning

confidence: 99%

Solving the double-banana rigidity problem: a loop-based approach

2016

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“…Keeping surgical tools centred in the field of view of the image is most important in MIS in order to reduce risks of damaging non-visible anatomical structures. Segmentation has been addressed using several approaches, using colour either on red, green, and blue (RGB) (17,18) or hue, saturation value (HSV) (19), space, geometrical properties such as border detection (20), or a combination of both (21).…”

Section: Laparoscopic Video Analysis: Literature Overviewmentioning

confidence: 99%

Laparoscopic video analysis for training and image-guided surgery

Sánchez-González

Cano

García

et al. 2011

Minimally Invasive Therapy & Allied Technologies

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Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery.tool tracking, organ reconstruction

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“…Furthermore, techniques for assisting the surgeon with visually guided instruments ( [5], [6], [7], [8]) and autonomously navigated endoscopic cameras have been developed (e.g. [9], [10]). For the reason of documenting and benchmarking surgical interventions, and to anticipate potential mistakes in the surgical workflow, modeling and analyzing these procedures has become an active field of research [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Contour-based surgical instrument tracking supported by kinematic prediction

Staub¹,

Lenz²,

Panin³

et al. 2010

2010 3rd IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics

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Abstract-Surgical tool tracking is an important key functionality for many high-level tasks in both robot-assisted and conventional minimally invasive surgery. Though the fields of application are similar in both surgery techniques (i.e. visually servoed instruments, workflow analysis or augmented reality), the kind of available information about the position and orientation of the surgical tool differ. In conventional laparoscopic surgery additional information to the images provides by the endoscopic camera can only be obtained by an external tracking system. In contrast, robotic systems provide angular informations from encoder readings that allow for a sufficient pose estimation and initialization of an image-based tracking algorithm. Our approach utilizes both encoder readings and visual information, in order to stabilize tracking in image space. The image-based tracking is supervised by means of the kinematic information and reinitialized in case of conflicting results. As tracking modality we utilize the Contracting Curve Density (CCD) algorithm that looks for maximal separation of local color statistics along the contour of a model.

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Automatic guidance of an assistant robot in laparoscopic surgery

Cited by 69 publications

References 2 publications

Solving the double-banana rigidity problem: a loop-based approach

Solving the double-banana rigidity problem: a loop-based approach

Laparoscopic video analysis for training and image-guided surgery

Contour-based surgical instrument tracking supported by kinematic prediction

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