Safety-Enhanced Motion Planning for Flexible Surgical Manipulator Using Neural Dynamics

Abstract
Robot-assisted systems have been developed for minimally invasive surgical procedures, which bring tremendous benefits for patients, such as less trauma, less bleeding, and shorter recovery time. Among the contemporary surgical robotic manipulators, flexible serpentine manipulator shows great advantages on operating with complicated nonlinear anatomical constraints, and it can reach deep occluded surgical targets without colliding in a critical anatomical environment. In surgical robotic operation, less spatial sweeping area from the flexible manipulator in motions is desired to induce the minimal surgical complications. The goal of our research is to reduce unnecessary sweeping motion of the flexible surgical manipulator in operations, and to obtain safer and more reliable reference trajectories. A novel 3-D neural dynamic model is proposed and expected to obtain the safety-enhanced trajectory in workspace with the consideration of minimum sweeping area. In this model, the neural stimulation propagates from the start state to the whole network through the connective weight of manipulator’s sweeping area. According to the results of comparative studies with commonly used planning algorithms in various simulation scenarios, the proposed planning algorithm is validated in terms of effectiveness and safety. Ultimately, the experiments on phantoms and preclinical cadaveric human head show the feasibility of the proposed safety-enhanced planning algorithm.
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Fault Diagnosis in Image-Based Visual Servoing with Eye-in-Hand Configuration Using Kalman Filter

Abstract
In this paper, the fault diagnosis (FD) problem in image-based visual servoing with eye-in-hand configurations is investigated. The potential failures are detected and isolated based on approximating parameters related. First, the failure scenarios of the visual servoing systems are reviewed and classified into the actuator and sensor faults. Second, a residual generator is proposed to detect the failure occurrences, based on the Kalman filter. Third, a decision table is proposed to isolate the fault type. Finally, simulation and experimental results are given to validate the efficacy and the efficiency of the proposed FD strategies.
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A Novel 4-DOF Hybrid Magnetic Bearing for DGMSCMG

Abstract
In this paper, a novel structure of four degrees of freedom (4DOF) hybrid magnetic bearing is proposed for double gimbal magnetically suspended control momentum gyro (DGMSCMG). It includes two active parts and one passive part, and every active part has eight stator magnetic poles around the circumference in X and Y directions, which are divided into upper and lower layers. The passive part has two whole magnetic rings, which is located in the middle of this 4DOF hybrid magnetic bearing. The radial active force is analyzed by equivalent magnetic circuit method (EMCM) and the axial resilience force is analyzed by the infinitesimal method based on the end magnetic flux. Meanwhile, 3-D finite element model of the 4DOF hybrid magnetic bearing is establish with ANSYS software, and the radial displacement versus radial force, the current versus radial force, and the axial displacement versus axial resilience force characteristics are analyzed compared with the EMCM. Furthermore, the 10Nms DGMSCMG prototype with proposed 4DOF hybrid magnetic bearing is manufactured, and the experiments of the radial active force test and the axial resilience force test are carried out. Experimental results show that the presented 4DOF hybrid magnetic bearing has good force performance and verify the correctness of the theoretical analysis.
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Towards transferring skills to flexible surgical robots with programming by demonstration and reinforcement learning

Abstract:
Flexible manipulators such as tendon-driven ser-
pentine manipulators perform better than traditional rigid
ones in minimally invasive surgical tasks, including navigation
in confined space through key-hole like incisions. However, due
to the inherent nonlinearities and model uncertainties, motion
control of such manipulators becomes extremely challenging.
In this work, a hybrid framework combining Programming by
Demonstration (PbD) and reinforcement learning is proposed
to solve this problem. Gaussian Mixture Models (GMM),
Gaussian Mixture Regression (GMR) and linear regression are
used to learn the inverse kinematic model of the manipulator
from human demonstrations. The learned model is used as
nominal model to calculate the output end-effector trajectories
of the manipulator. Two surgical tasks are performed to
demonstrate the effectiveness of reinforcement learning: tube
insertion and circle following. Gaussian noise is introduced to
the standard model and the disturbed models are fed to the
manipulator to calculate the actuator input with respect to the
task specific end-effector trajectories. An expectation
maximization (E-M) based reinforcement learning algorithm is
used to update the disturbed model with returns from rollouts.
Simulation results have verified that the disturbed model can
be converged to the standard one and the tracking accuracy is
enhanced.
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Robust Fault-Tolerant Control for a Class of Second-Order Nonlinear Systems Using an Adaptive Third-Order Sliding Mode Control

Abstract
Due to the robustness against the uncertainties, conventional sliding mode control (SMC) has been extensively developed for fault-tolerant control (FTC) system. However, the FTCs based on conventional SMC provide several disadvantages such as large transient state error, less robustness, and large chattering , that limit its application for real application. In order to enhance the performance, a novel adaptive third-order SMC, which combines a novel third-order sliding mode surface, a continuous strategy and an adaptation law, is proposed. Compared with other innovation approaches, the proposed controller has an excellent capability to tackle several types of actuator faults with an enhancing on robustness, precision, chattering reduction, and time of convergence. The proposed method is then applied for an attitude control of a spacecraft and the results demonstrate the superior performance. Index Terms—Fault diagnosis, fault-tolerant control (FTC), high-order sliding mode (HOSM) control, nonlinear systems, observer-controller system.
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Fault-Tolerant Inverter for High-Speed Low-Inductance BLDC Drives in Aerospace Applications

Abstract
Due to the simplicity and high reliability, brushless dc (BLDC) motors are widely used in space application. High-reliability levels are the vital aspect for ensuring the long-term stable operation of the BLDC motor system, which is used in aerospace applications. The fault-tolerant control of the BLDC motor is of great importance for its continuous operating capacity even under the faulty situation. This paper proposes a fault-tolerant topology composed of an additional phase leg and a fault-protective circuit for the high-speed low-inductance BLDC motor. Based on the analysis of the overcurrent and overvoltage phenomenon after the switch faults, a novel fault isolation and system reconfiguration method is presented. The method can achieve safe isolation and reconfiguration to avoid the secondary fault caused by direct switch of the redundant switch and the faulty switch after the fault-diagnosis process. Both simulation and experimental results confirm the feasibility and effectiveness of the proposed method.
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Preliminary development of a soft robotic ultrasound steering system

Abstract:
Intravascular ultrasound (IVUS) imaging provides
two-dimensional (2D) real-time luminal and transmural
cross-sectional images of intravascular vessels with detailed
pathological information. It has offered significant advantages
in terms of diagnosis and guidance and has been increasingly
introduced from coronary interventions into more generalized
endovascular surgery. However, IVUS itself does not provide
spatial pose information for its generated images, making it
difficult to construct a 3D intravascular visualization. To
address this limitation, IVUS imaging-driven 3D intravascular
reconstruction techniques have been developed. These
techniques enable accuratediagnosis andquantitative
measurements of intravascular diseases to facilitate optimal
treatment determination. Such reconstruction extends the IVUS
imaging modality from pure diagnostic assistance to
intraoperative navigation and guidance and supports both
therapeutic options and interventional operations. This paper
presents a comprehensive survey of technological advances and
recent progress on IVUS imaging-based 3D intravascular
reconstruction and its state-of-the-art applications. Limitations
of existing technologies and prospects of new technologies are
also discussed.
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Self-correction of Commutation Point for High-speed Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF

Abstract
This paper presents a novel self-correction method of commutation point for high-speed sensorless brushless dc motors with low inductance and nonideal back electromotive force (EMF) in order to achieve low steady-state loss of magnetically suspended control moment gyro. The commutation point before correction is obtained by detecting the phase of EMF zero-crossing point and then delaying 30 electrical degrees. Since the speed variation is small between adjacent commutation points, the difference of the nonenergized phase’s terminal voltage between the beginning and the end of commutation is mainly related to the commutation error. A novel control method based on model-free adaptive control is proposed, and the delay degree is corrected by the controller in real time. Both the simulation and experimental results show that the proposed correction method can achieve ideal commutation effect within the entire operating speed range
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Motion Planning based on Learning from Demonstration for Multiple-Segment Flexible Robots Actuated by Electroactive Polymers

Abstract
Multiple-segment flexible and soft robotic arms composed by ionic polymer – metal composite (IPMC) flexible actuators exhibit compliance but suffer from the difficulty of path planning due to their redundant degrees of freedom, although they are promising in complex tasks such as crossing body cavities to grasp objects. We propose a learning from demonstration method to plan the motion paths of IPMC-based manipulators, by statistics machine-learning algorithms. To encode demonstrated trajectories and estimate suitable paths for the manipulators to reproduce the task, models are built based on Gaussian mixture model and Gaussian mixture regression, respectively. The forward and inverse kinematic models of IPMC-based soft robotic arm are derived for the motion control. A flexible and soft robotic manipulator is implemented with six IPMC segments, and it verifies the learned paths by successfully completing a representative task of navigating through a narrow keyhole.
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A novel constrained wire-driven flexible mechanism and its kinematic analysis

Abstract
Snake-like flexible manipulators are widely used in minimally invasive surgery (MIS), which require adequate dexterity in confined workspace. Typically, the design mechanisms of these manipulators include tendon-driven mechanism and concentric tube mechanism. Though, the workspace and dexterity of these designs are limited due to the lack of control in either the length of the bending section or the curvature of the bending section at the distal end. In this paper, we present a novel constrained wire-driven flexible mechanism (CWFM), in which both the length and the curvature of the bending section are controllable. The idea is to employ an active constraint to control the length of the bending section and use the wires to control the curvature of the bending section. Compared to the existing designs based on wire-driven flexible mechanism (WFM), CWFM has expanded workspace and enhanced dexterity while its size is not sacrificed. Additional benefits include much reduced sweeping area and controllable stiffness. Based on the computer simulation, on average, CWFM with the same size as WFM can improve the dexterity by 4.69 times and reduce the sweeping area to 20.5%.
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