Inverse Kinematics for Steerable Concentric Continuum Robots

Kolpashchikov, Dmitrii Yu.; Danilov, Viacheslav V.; Laptev, Nikita V.; Skirnevskiy, Igor; Manakov, Roman A.; Gerget, Olga; Meshcheryakov, Roman

doi:10.1007/978-981-13-9267-2_8

Cited by 10 publications

(6 citation statements)

References 15 publications

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“…The catheter configuration is generated using forward kinematics algorithms, which build an axial spline, as well as the vector functionD(z) for the given bending angles and the lengths of the corresponding catheter joints. The kinematics algorithm of the catheter is described in more detail in (Kolpashchikov et al, 2018).…”

Section: Data Synthesismentioning

confidence: 99%

Boosting Segmentation Accuracy of the Deep Learning Models Based on the Synthetic Data Generation

Danilov

Gerget

Kolpashchikov

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In the era of data-driven machine learning algorithms, data represents a new oil. The application of machine learning algorithms shows they need large heterogeneous datasets that crucially are correctly labeled. However, data collection and its labeling are time-consuming and labor-intensive processes. A particular task we solve using machine learning is related to the segmentation of medical devices in echocardiographic images during minimally invasive surgery. However, the lack of data motivated us to develop an algorithm generating synthetic samples based on real datasets. The concept of this algorithm is to place a medical device (catheter) in an empty cavity of an anatomical structure, for example, in a heart chamber, and then transform it. To create random transformations of the catheter, the algorithm uses a coordinate system that uniquely identifies each point regardless of the bend and the shape of the object. It is proposed to take a cylindrical coordinate system as a basis, modifying it by replacing the Z-axis with a spline along which the h-coordinate is measured. Having used the proposed algorithm, we generated new images with the catheter inserted into different heart cavities while varying its location and shape. Afterward, we compared the results of deep neural networks trained on the datasets comprised of real and synthetic data. The network trained on both real and synthetic datasets performed more accurate segmentation than the model trained only on real data. For instance, modified U-net trained on combined datasets performed segmentation with the Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real samples achieved the level of 86.5±3.6%. Using a synthetic dataset allowed decreasing the accuracy spread and improving the generalization of the model. It is worth noting that the proposed algorithm allows reducing subjectivity, minimizing the labeling routine, increasing the number of samples, and improving the heterogeneity.

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Section: Data Synthesismentioning

confidence: 99%

Boosting Segmentation Accuracy of the Deep Learning Models Based on the Synthetic Data Generation

Danilov

Gerget

Kolpashchikov

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Inverse Jacobian based IK approach has higher computation cost and also have singularity problems. Therefore, Forward and Backward Recursive Inverse Kinematics (FABRIK) can be used to find the IK solution [12][13][14].…”

Section: Inverse Kinematicsmentioning

confidence: 99%

Kinematics of Three Segment Continuum Robot for Surgical Application

Bamoriya

Kumar

2021

Lecture Notes in Mechanical Engineering

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We propose a design of a multi-segment continuum robot with inherent compliance and flexibility for minimally invasive surgical applications. The multisegment continuum robot can be guided, with high dexterity, through complex curvilinear paths. Thus, they are ideal for endoscopic and surgical procedures. In this paper, we are discussing the kinematics of a three-segment tendon-driven continuum robot. We derived the forward and inverse kinematics for the proposed continuum robot, show the workspace analysis and quantify the robot reach and dexterity. Further, we discuss the kinematic advantages and disadvantages of tendon-driven three-segment continuum robot versus double-segment continuum robot.

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“…Serpentine robot is snake-like robot composed of multiple small links connected by Hooke's hinge, such as the segmented linkage robot designed by Tianliang Liu [1]. Continuum robots [2][3][4] are a jointless robot arm with a biological structure similar to an elephant trunk such as the elephant trunk-like robot developed by Walker [5], the BHA [6]. If the material of the continuum robot is soft, it can be classified as an emerging class of bionic soft robots in recent years.…”

Section: Introductionmentioning

confidence: 99%

Shape-controllable inverse kinematics of hyper-redundant robots based on the improved FABRIK method

Niu,

Han,

Huang

et al. 2023

Robotica

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Hyper-redundant robots have good prospects for applications in confined space due to their high flexibility and slim body size. However, the super-redundant structure brings great challenges for its inverse kinematics with shape constraints. Unfortunately, traditional Jacobian pseudo-inverse-based inverse kinematics method and forward and backward reaching inverse kinematics (FABRIK) method are difficult to constrain the arm shape and realize trajectory tracking in confined spaces. To solve this problem, we propose a shape-controllable FABRIK method to satisfy the given path and shape constraints. Firstly, the kinematic model of the hyper-redundant robot is established, and the canonical FABRIK method is introduced. Based on the preliminary works, the single-layer improved FABRIK method is developed to solve the position and pointing inverse kinematics considering path environment and joint angle constraints instead of two-layer geometric iterations. For tracking the desired end roll angles, the polygonal virtual arm is designed. The real arm roll angle is achieved by controlling its winding on the virtual arm. In this way, the shape can be controlled. Finally, we compare the proposed method with other three approaches by simulations. Results show that the proposed method is more efficient and the arm shape is controllable.

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Inverse Kinematics for Steerable Concentric Continuum Robots

Cited by 10 publications

References 15 publications

Boosting Segmentation Accuracy of the Deep Learning Models Based on the Synthetic Data Generation

Boosting Segmentation Accuracy of the Deep Learning Models Based on the Synthetic Data Generation

Kinematics of Three Segment Continuum Robot for Surgical Application

Shape-controllable inverse kinematics of hyper-redundant robots based on the improved FABRIK method

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