Autonomous Control of Continuum Robot Manipulators for Complex Cardiac Ablation Tasks

Although tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

Analysis of deformation and multi-angle tip contact in a combined mechanism of thin-walled membrane forces and inextensible layers in soft actuators

Exerting forces and torques instantaneously on rigid magnetic bodies with no physical connection is an attractive feature of magnetic robotics. This demonstrates great potential for manipulating tools that are externally controlled through the use of magnetic fields in minimally invasive surgeries. The magnetic field can be controlled by the application of currents to electromagnets positioned around the surgical site, and the necessary currents for a specific desired manipulation can be derived from magnetic field models. However, the magnetic field generated by electromagnetic coils are highly nonlinear, especially in the vicinity of the magnetic field sources, which complicates the modeling process. While simple dipole models provide a good approximation for these fields far away from the electromagnets, these models tend to be highly inaccurate near the sources. Magnetic surgical applications benefit from models which accurately describe fields and gradients both near and far from the field source. Particularly, since forces and torques decay inversely proportionally with the cube of the distance to the coil, inaccurate modeling near the coil makes large regions near the coil unfit for applications requiring precisely predicted motion. Estimation errors near coils generate inaccuracies in field models that significantly reduce control performance for rigid magnetic bodies. In order to tackle this problem, we utilize Zernike basis functions to analytically represent the nonlinear magnetic field distribution more accurately. The accuracy of the controller is tested experimentally by driving a magnetic surgical suture needle with a length of 22 mm in the MagnetoSuture™ system along a lemniscate trajectory. The magnetic needle's tip position and the needle orientation, autonomously controlled by the proposed controller, shows RMS tracking error of 2.35 mm using typical dipole models and 1.71 mm for the Zernike fitting approach, a 27% improvement in tracking error. This suggests that the use of Zernike basis functions to capture the nonlinearities of the magnetic field may assist in implementing fast and precise autonomous control strategies for magnetic suture needles.

Adaptive fuzzy fault-tolerant output feedback control of uncertain nonlinear systems with actuator faults based on dynamic surface technique

In this paper, an image-based impedance control strategy for force tracking of an unmanned aerial manipulator (UAM) is presented. Firstly, image features with nice decoupling characteristics are designed and the relationship between the camera motion and the image features is derived. Then, a two-stage strategy is proposed to achieve force tracking of the UAM on a planar object in an arbitrary pose. The first stage drives the end-effector perpendicular to the object’s planer surface by pure visual servoing. To achieve force tracking under the visual guidance, an adaptive visual impedance control method which adjusts the target stiffness according to the force tracking error and the visual feature error is proposed in the second stage. The closed-loop system is proved asymptotically stable by means of Lyapunov analysis. Further, the stability in free flight phase of the stage two is also analyzed and ensured. Finally, experiments were carried out including a whiteboard cleaning task in different poses. The experimental results illustrate the validity and effectiveness of the proposed approach. Note to Practitioners—This work is motivated by the contact force tracking problem of an unmanned aerial manipulator (UAM) without the position measurement. In recent years, impedance control is widely used in force tracking problems. However, position measurement is needed both for the robot and the object in most studies. To generate desired force under visual guidance, image-based visual servoing is combined with a novel impedance controller with variable stiffness. The idea is intuitive. For a human in contact with a wall, the contact force is controlled by adjusting his arm stiffness, which means the stiffness is adapted to the difference between the desired and actual contact force. Furthermore, the stiffness is also adapted to the visual tracking error, removing the assumption that no tracking error is in the inner loop of the impedance controller in the previous works. The proposed strategy is a promising solution for real applications and is validated by a board cleaning experiment in this paper.

Continuum manipulators can operate in complex environments where traditional rigid manipulators fail. However, the modeling of inverse kinematics remains challenging because of its inherent nonlinearities and various external conditions. This work proposes an online learning control framework with a data cache pool utilizing a constant‐curvature model inspired neural network (CCMINN) model to obtain the inverse kinematics model of tendon‐actuated continuum manipulators. The CCMINN model is a kind of geometric relationships‐inspired neural network, which is inspired by the geometric relationships within the constant‐curvature model. This model improves the ability of traditional fully connected neural network models on high convergence speed and precision through its constant‐curvature inspiration layers. These layers embed geometry insights into the neural network structure rather than loss functions like physics‐informed neural networks. The online learning framework enables CCMINN to maintain high control accuracy in a variety of external load scenarios. Experiments show average tracking errors of 1.4 mm, 1.38 mm, and 1.48 mm (0.7%, 0.64%, and 0.74% of the continuum manipulator length) in the free space, under constant and variable loading conditions, respectively. The results show that combining the fast‐converging CCMINN with an online learning control framework enables high‐precision and robust positioning control of continuum manipulators under various external payloads.

Underwater vehicle-manipulator systems (UVMSs) are underwater robots equipped with one or more manipulators to perform intervention missions. This article provides the mechanical, electrical, and software design of a novel UVMS equipped with a continuum manipulator, referred to as a continuum-UVMS. A kinematic model for the continuum-UVMS is derived in order to build an algorithm to resolve the robot’s redundancy and generate joint space commands. Different methods to optimize the trajectory for specific tasks are proposed using both the weighted least norm solution and the gradient projection method. Kinematic simulation results are analyzed to assess the performance of the proposed algorithm. Finally, the continuum-UVMS is deployed in an experimental demonstration in which both teleoperation and autonomous control are tested for a given reference trajectory.

In conventional core decompression of osteonecrosis, surgeons cannot successfully reach the whole area of the femoral head due to rigidity of the instruments currently used. To address this issue, we present design and fabrication of a novel steerable drill using a continuum dexterous manipulator (CDM) and two different flexible cutting tools passing through the lumen of the CDM. A set of experiments investigated functionality and efficiency of the curved-drilling approach and the flexible tools on simulated cancellous bone. Geometry of the cutter head, rotational and feed velocity of the tool, and pulling tension of the CDM cables have been identified as the effective curved-drilling parameters. Considering these parameters, we investigated drilling trajectory, contact force, and mass removal for various combinations of feed-velocities (0.05, 0.10, and 0.15 mm/s) and cable tensions (6, 10, 15, and 25 N) with constant rotational speed of 2250 r/min. Results show that: first, pulling tension of the cable is the most dominant parameter affecting the curved-drilling trajectory; and second, the proposed steerable drill is able to achieve 40° bend without buckling. Based on these results we developed a method for planning drill trajectories and successfully verified abilities for S-shape and multiple-branch drilling. The verification experiments were performed on both simulated and human cadaveric bones.

In this paper we continue the investigations begun in [13] and [14] on the control of robot manipulators. Using the theory of uncertain dynamical systems developed in [4], [5], [16], [17] we derive a robust nonlinear control strategy with guaranteed tracking properties which can be quantified given bounds on the extent of model uncertainty, sensor noise, input disturbances, etc. We also extend the class of pointwise optimal control strategies of [13] to the case of systems with uncertainty in order to treat the problem of input constraints within the context of uncertain systems.

Precise position force control is the core and difficulty of robot technology, especially for robots with redundant degrees of freedom. For example, track-based controls often fail to grind the robot due to the intolerable impact force applied to the end-effector. The main difficulties lie in the coupling of motion and contact forces, redundancy analysis and physical constraints. In this chapter, we propose a new motion force control strategy under the framework of recursive neural network. The tracking error and contact force are described respectively in the orthogonal space. By choosing the minimum joint torque as the secondary task, the control problem is transformed into the QP problem under multi-constraint conditions. In order to obtain real-time optimization of the joint torque relative to the non-convex joint angle, the original QP is reconstructed at the velocity level, and the original objective function is replaced by the time derivative. Then a convergent dynamic neural network is established to solve the improved QP problem online. The robot position control based on recursive neural network is extended to the robot position control based on position force, which opens a new way for the robot to turn from simple control angle to crossover design with convergence and optimality. Numerical results show that the proposed method can realize precise position force control, deal with inequality constraints such as joint angular velocity and torque limitation, and reduce joint torque consumption by 16% on average.

This work presents the design and the corresponding stability analysis of a model-based, joint position tracking error constrained, adaptive output feedback (OFB) controller for robot manipulators. Specifically, provided that the initial joint position tracking error starts within a predefined region, the proposed controller algorithm ensures that the joint tracking error remains inside this region and asymptotically approaches zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The constraint imposed on the position tracking error term ensures predictable overshoot for the overall system and enables a predetermined transient performance. The need for the joint velocity measurements is removed via the use of a surrogate filter formulation in conjunction with the use of desired model compensation. The stability and the convergence of the closed-loop system are proved via a barrier Lyapunov function (BLF)-based argument. Extensive numerical simulations and experimental studies performed on a two-link, direct-drive robotic manipulator are provided to illustrate the feasibility and effectiveness of the proposed method.

This article aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: 1) the faster response, lower tracking errors, lower chattering, and higher robustness of the FTC, 2) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and 3) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analyzed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

Continuum manipulator compliance enables operation in delicate environments while challenging the actuation and control approaches. In the case of a catheter ablation of atrial fibrillation, the compliance of the continuum backbone lends an inherent safety to the device. This inherent safety frustrates attempts at precise, accurate, and fast control, limiting the continuum devices to simple and static positioning tasks. This paper develops interleaved continuum-rigid manipulation, by which the hysteretic nonlinearities encountered in tendon-actuated continuum manipulators are compensated by the discrete rigid joints located between the continuum sections. The rigid joints introduce an actuation redundancy, which an interleaved controller may use to avoid the continuum nonlinearities and dynamic excitations, or to prefer particular configurations that may improve task accuracy, permit greater end-effector forces, or avoid environment obstacles. Two experimental systems explore the potential of these joints to: 1) increase the manipulator's dexterous workspace, and 2) correct for actuation nonlinearities and enhance manipulator performance. These experiments also expose important design and control observations that were not apparent in the general robotic and continuum literature.

In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.

In this article, an adaptive dynamic surface control approach is proposed for uncertain strict‐feedback systems (SFSs) to guarantee both the prescribed transient tracking performance and the asymptotic tracking while realizing the accurate parameter estimation. It is assumed that SFSs are subject to linearly parametric uncertainties in both the drift terms and the control coefficients. A new inequality on the arctangent function, which can be widely used in the robust or the adaptive control designs, is established. Owing to this inequality, nonlinear robust filters with arctangent functions are designed and embedded into the backstepping control algorithm to avoid the “differential explosion” problem. Moreover, an improved forgetting‐factor‐based parameter estimation error reconstruction mechanism is proposed. And the obtained parameter estimation errors are integrated into the adaptive laws to achieve the accurate parameter estimation. Furthermore, the projection operator is applied to avoid the singularity problem of the control law. Besides, an asymmetric error transformation is introduced to restrict the tracking error within the prescribed performance envelope. It is proved that the tracking error and the parameter estimation errors asymptotically converge to zero and the tracking error satisfies the prescribed transient performance. Finally, the effectiveness of the proposed control approach is validated in terms of the single‐link manipulator actuated by a brush DC motor.