Concentric Tube Robots: Rapid, Stable Path-Planning and Guidance for Surgical Use

Leibrandt, Konrad; Bergeles, Christos; Yang, Guang‐Zhong

doi:10.1109/mra.2017.2680546

Cited by 49 publications

(24 citation statements)

References 20 publications

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“…A straightforward approach is to approximate the Jacobian of the robot by finite differences on the solution of the BVP problem, e.g. [7]. Rucker et al [8] proposed a numerical approach to increase the computational efficiency of Jacobian estimation for a CTR under external loading.…”

Section: Introductionmentioning

confidence: 99%

“…The contents may change prior to publication. fills a gap between the locally intelligent and rapid Jacobian-based controllers, and the globally intelligent but computation-heavy pathplanners [7], [16]. Additionally, the proposed MPC is designed such that it only requires the solution of the CTR model as an initial value problem (IVP) rather than a BVP.…”

Section: Introductionmentioning

confidence: 99%

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Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control

et al. 2020

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This paper presents a Model Predictive Controller (MPC) developed for the autonomous steering of concentric tube robots (CTRs). State-of-the-art CTR control relies on differential kinematics developed by local linearization of the CTR's mechanics model and cannot explicitly handle constraints on robot's joint limits or unstable configurations commonly known as snapping points. The proposed nonlinear MPC explicitly considers constraints on the robot configuration space (i.e. joint limits) and the robot's workspace (i.e. mixed boundary conditions on robot curvature). Additionally, the MPC calculates control decisions by optimizing the model-based predictions of future robot configurations. This way, it avoids configurations it cannot recover from, joint limits, singular configurations and snapping. The proposed controller is evaluated via simulations and experimental studies with a variety of trajectories of increasing complexity. Simulation results demonstrate the capability of MPC to avoid singularities while satisfying robot mechanical constraints. Experimental results demonstrate that our solution enables following of trajectories unattainable by state-of-the-art controllers with mean error corresponding to 1% of robot arclength.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control

et al. 2020

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“…Torres et al successfully implemented the collision-free motion path planning of a concentric tube robot in neurosurgery based on RRT (rapidly exploring random tree) algorithm [21]- [23]. Leibrandt et al achieved fast and stable path planning of a concentric tube robot based on PRM (probabilistic road maps) algorithm combined with kinematic look-up tables [24]. Park et al [25] used the RRT algorithm to obtain the motion trajectories for a da Vinci surgical robot system, which improved the automation level of the endoscopic camera manipulator and relieved surgeon's workload in surgery.…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

Zhang

et al. 2020

IEEE Access

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In robot-assisted endoscopic nasal surgery, due to the slender and complicated nasal cavity and sinus anatomical structure, the nasal endoscope of robot end-effector is prone to injure surrounding tissues during surgical approaches. In order to improve the movement safety of the endoscope in robot-assisted endoscopic nasal surgery, a path planning method for endoscopic surgical approaches based on medical image pixel map searching is proposed in this paper, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Firstly, through the trajectory analysis of nasal endoscope tip during surgery, the path planning requirements of endoscope tip are analyzed and obtained. Then, considering the slender and complicated anatomy of the nasal cavity, a binarized three-dimensional grid map containing the spatial anatomy of the nasal cavity is constructed based on the patient's CT medical image sequence. The endoscopic surgical approaches are searched and optimized by introducing the A-star algorithm, and safe surgical paths from the nasal entrance point to the operating areas are obtained. Finally, a virtual nasal endoscopy system is developed and tested on a head model containing nasal tissue, and the effectiveness of the planned surgical paths is verified by automatic virtual nasal endoscopy browsing experiment.

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“…Bergeles et al, [1] proposed a methodology for operating CTRs under anatomical constraints in their stable configuration via preoperative path planning. Similarly, Leibrandt et al developed software for open-loop CTR control that avoids instabilities online [11]. The user is haptically directed towards precomputed paths that do not contain instabilities in real-time, but a static anatomy is assumed.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Steering of Concentric Tube Robots for Enhanced Force/Velocity Manipulability

Khadem

O'Neill

Mitros

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Concentric tube robots (CTR) can traverse tightly curved paths and offer dexterity in constrained environments, making them advantageous for minimally invasive surgical scenarios that experience strict anatomical and surgical constraints. Their shape is controlled via rotation and translation of several concentrically arranged super-elastic precurved tubes that form the robot backbone. As the elastic energy accumulated in the backbone due to bending and twist of the tubes increases, robots can exhibit sudden snapping motions, which can damage the surrounding tissues. In this paper, we proposed an approach for closed-loop steering of a redundant CTR that allows for snap-free motion and enhances its force/velocity manipulability, increasing the capacity of the robot to move and/or exercise forces along any direction. First, a controller stabilizes the CTR end-effector on a desired time-variant trajectory. Next, an online optimizer uses the robot's redundant Degrees of Freedom (DoF) to reshape its manipulability in real-time and steer it away from potentially snapping configurations or increase its capacity in delivering force payloads. Simulations and experiments demonstrate the performance of the proposed control strategy. The controller can steer a generally unstable CTR along trajectories while avoiding instabilities with a mean error of 850 µm, corresponding to 0.6% of arclength, and improves robot ability to exercise forces by 55%.

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Concentric Tube Robots: Rapid, Stable Path-Planning and Guidance for Surgical Use

Cited by 49 publications

References 20 publications

Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control

Autonomous Steering of Concentric Tube Robots via Nonlinear Model Predictive Control

Endoscopic Path Planning in Robot-Assisted Endoscopic Nasal Surgery

Autonomous Steering of Concentric Tube Robots for Enhanced Force/Velocity Manipulability

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