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Photodynamic therapy (PDT) has received increased attention over the past decades with the potential to non-invasively treat cancer via light exposure. Due to the limited light penetration depth (typically less than 1 cm), the PDT optical fiber typically needs to be placed close to the cancerous region to ensure the treatment outcome. In this paper, we present a novel soft robot laparoscope design that enables accurate and safe deployment of PDT optical fiber for pancreatic tumor treatment. To achieve this, the proposed soft robot is able to produce 3 degrees of freedom (DoF) motions: linear translation, in-plane bending, and axial rotation. A micro camera is integrated with the soft robot to provide intraoperative image guidance during the procedure. The robot forward kinematics model was obtained via a generalized calibration method, and the differential kinematics was derived analytically. Benchtop characterization results indicated that the soft robot was able to achieve 1.4 $\pm$ 0.4 mm position and 1.5 $\pm$ 1.1 degrees orientation accuracy, respectively. The robot was experimentally validated in two \textit{in vivo} mice models, and the results indicated that the proposed system successfully reduced the tumor size from 15.3 $mm^3$ and 11.8 $mm^3$ to 12.1 $mm^3$ and 4.4 $mm^3$ respectively.

Vitreoretinal surgery is among the most delicate surgical tasks during which surgeon hand tremor may severely attenuate surgeon performance. Robotic assistance has been demonstrated to be beneficial in diminishing hand tremor. Among the requirements for reliable assistance from the robot is to provide precise measurements of system states e.g. sclera forces, tool tip position and tool insertion depth. Providing this and other sensing information using existing technology would contribute towards development and implementation of autonomous robot-assisted tasks in retinal surgery such as laser ablation, guided suture placement/assisted needle vessel cannulation, among other applications. In the present work, we use a state-estimating Kalman filtering (KF) to improve the tool tip position and insertion depth estimates, which used to be purely obtained by robot forward kinematics (FWK) and direct sensor measurements, respectively. To improve tool tip localization, in addition to robot FWK, we also use sclera force measurements along with beam theory to account for tool deflection. For insertion depth, the robot FWK is combined with sensor measurements for the cases where sensor measurements are not reliable enough. The improved tool tip position and insertion depth measurements are validated using a stereo camera system through preliminary experiments and a case study. The results indicate that the tool tip position and insertion depth measurements are significantly improved by 77% and 94% after applying KF, respectively.

The laryngeal surgery is usually performed with rigid surgical instruments. However, those instruments inevitably cause tissue damage. A novel notch continuum manipulator for laryngeal surgery is proposed. The deformation of the notch continuum manipulator is highly nonlinear. The controller is difficult to construct. The model-based modeling strategy from micro to macro is adopted for forward kinematics. The proposed notch continuum manipulator is modeled as a series of flexible units. Based on the mechanical model and Denavit–Hartenberg method, the mapping from the driven space to the Cartesian space is constructed. The model-free strategy based on the forward kinematics and machine learning is adopted to provide an approximate solution for the inverse kinematics. Therefore, combining the advantages of model-free and model-based strategies, a hybrid method can be proposed to accurately model and solve the kinematics of a class of continuum manipulators. The experimental results obtained through the prototype show that the proposed controller model can meet the actual engineering requirement.

We consider the problem of tool position and orientation state estimation for robot manipulators in workspace by sensor fusion of the internal robot joint measurements with inertial measurement unit data. A prerequisite for this to be successful is accurate calibration of the sensors used. Therefore, we discuss a method for calibration of the sensor with respect to the robot end-effector, which is straightforward to apply on an arbitrary industrial manipulator. We also consider two different workspace state-estimation algorithms requiring a minimum of robot modeling; the first is based on the extended Kalman filter and the second is based on the Rao–Blackwellized particle filter. The calibration procedure and the state-estimation algorithms were evaluated and compared in extensive experiments. Both state-estimation algorithms exhibited an accuracy improvement compared to estimates provided by the forward kinematics of the robot. Moreover, both algorithms were shown to satisfy the constraints of real-time execution at 4-ms sampling period. To further evaluate and compare the robustness of the methods, the algorithms were investigated with respect to the sensitivity of the filter parameters and the noise modeling.

This paper presents the design of a double triangular parallel mechanism for precision positioning and large force generation. In recent years, with the acceleration of miniaturization in mobile appliances, the demand for precision aligning and bonding has been increasing.Such processes require both high precision and large force generation, which are difficult to obtain simultaneously. This study aimed at constructing a precision stage that can perform submicrometer resolution alignment, several-hundred micrometer stroke, and several-hundred Newton force generation.Piezoelectric actuators were tactically placed to constitute a parallel mechanism with a double triangular configuration. In addition, flexure hinges were carefully designed and optimized. The three actuators in the inner triangle function as an in-plane positioner, whereas the three actuators in the outer triangle as an out-of-plane positioner. To facilitate rapid control of the developed stage, two mapping matrices were derived from the inverse and forward kinematics and the corresponding iterative numerical calculations. Finite-element analyses and experimental results proved that the developed stage with the double triangular configuration sufficiently met the requirements of precision positioning and large force generation.

Forceps tracking is an important element of high-level surgical assistance such as visual servoing and motion analysis. This paper describes a robust, efficient tracking algorithm capable of estimating the forceps tip position in an image space by fusing visual tracking data with kinematic information. In visual tracking, the full-state parameters of forceps are estimated using the projective contour models of a 3-D CAD model of the forceps. The likelihood of the contour model is measured using the distance transformation to enable fast calculation, and the particle filter estimates the full state of the forceps. For more robust tracking, the result data obtained from visual tracking are combined with kinematic data that are obtained by forward kinematics and hand-eye transformation. The fusion of visual and kinematic tracking data is performed using an adaptive Kalman filter, and the fused tracking enables the reinitialization of visual tracking parameters when a failure occurs. Experimental results indicate that the proposed method is accurate and robust to image noise, and forceps tracking was successfully carried out even when the forceps was out of view.

This paper addresses the complex task of unscrewing and screwing objects with a dexterous anthropomorphic robotic hand in two cases: with the first finger and thumb and also with the little finger and thumb. To develop an anthropomorphic solution, human finger synergies from nine test subjects were recorded while unscrewing and screwing a threaded cap. Human results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for application to a robotic hand. Because the joint trajectories exhibited the same frequency, a family of sinusoids that share a common time vector can be used in the path planning of the robotic hand to unscrew and screw objects. Additionally, the human unscrewing data are highly similar to the mirror image of the screwing data. This chiastic trait enables screwing to be performed by decreasing the time vector; increasing the time vector produces unscrewing. These factors significantly reduce the computational cost and complexity of the task. Cartesian and joint space error analyses show that the developed sinusoidal trajectories closely mimic the motion profiles seen in the human experiments. Furthermore, this bioinspired sinusoidal solution is extended to objects with wide variations in diameters by relating joint angle offsets of the robotic hand to object diameter size through the forward kinematics equations. The sinusoidal trajectories are all implemented within a PID sliding mode controller for a dexterous artificial hand to ensure overall system stability. Using the bioinspired sinusoidal joint angle trajectories, the robotic hand successfully unscrewed and screwed four different objects in all trials conducted with each object diameter size.

An adaptive estimation method is developed to generate real-time estimates of the position and orientation of the end-effector of an industrial robot and estimates of the root mean squared errors in these estimates, using real-time measurements of the position of a point on the end-effector, in addition to the usual measurements of the joint positions. To compensate for the lack of real-time measurements of the orientation of the end-effector, a Kalman filter is used to update a lookup table model of the kinematics of the robot that most affect the orientation of the end-effector. Measurements of the positions of a point on the end-effector collected during a short sequence of motions of the last axis of the robot are used by the Kalman filter to update this lookup table model. The updated lookup table model, together with real-time measurements of the position of a point on the end-effector and real-time measurements of the joint positions, is used by the estimator to compensate for the effects of geometric errors in the robot's structure and temperature variations on the position and orientation of the end-effector. In an application to a six-axis industrial robot, the adaptive estimation method is shown to substantially outperform the direct forward kinematics method whereby the position and orientation of the end-effector are estimated based upon joint position measurements alone.

This paper concerns the proposal and analysis of a novel spatial parallel manipulator. The parallel manipulator consists of a base plate, a movable platform, and three connecting legs. The moving platform has three degrees of freedom (DoFs), which are two degrees of translational freedom and one degree of rotational freedom, with respect to the base plate. The inverse and forward kinematics problems are described in closed forms and the velocity equation of the new parallel manipulator is given. Three kinds of singularities are presented. The workspace for the manipulator is analyzed systematically. Especially, the indices to evaluate the rotational capability of the moving platform of the manipulator will be defined and discussed in detail. Due to the high rotational capability performance, the proposed manipulator has wide application in the fields of industrial robots, simulators, micromotion manipulators, and parallel kinematic machines.

This paper presents a framework for generating optimal motor trajectories for a spherical parallel manipulator (SPM) with revolute joints, actuated by servomotors with default internal position control settings. The proposed framework consists of three phases. First, an approach to obtain unique forward kinematics is introduced, in order to relate the angular positions of the servomotors to the orientation of the SPM top mobile platform. Then, a configuration space for the SPM is defined by using numerical procedures, in order to guarantee the absence of singularities and of collisions between links during the motion of the manipulator. Finally, reference trajectories of the servomotors are defined via convex optimization. These trajectories determine an optimal evolution of the SPM motion based on the configuration space and original servomotor dynamics. The proposed strategy is experimentally demonstrated on a prototype of Agile Wrist SPM with three servomotors.