In physical human-robot interaction (pHRI) enabled by admittance control, delay-induced oscillations arising from both the neuromuscular time-delays of the human and electromechanical delays of the robot can cause unsafe instability in the system. This study presents and evaluates rate-limiting as a means to overcome such instability, and provides a new perspective on how rate-limiting can benefit pHRI. Specifically, a rate-limited and time-delayed human-in-the-loop (HITL) model is analyzed to show not only how the rate-limiter can transform an unstable equilibrium (due to time-delay) into a stable limit-cycle, but also how a desired upper-bound on the range of persistent oscillations can be achieved by appropriately setting the rate-limiter threshold. In addition, a study involving 10 subjects and the EXO-UL8 upper-limb exoskeleton, and consisting of 16 trials - 4 rate-limiter thresholds by 4 time-delays - is performed to: (1) validate the relationships between time-delays, rate-limits, and position bounds on persistent oscillations, and (2) demonstrate the effectiveness of rate-limiting for recovery from delay-induced oscillations without interfering with regular operation. Agreement of experimental results with the theoretical developments supports the feasibility of incorporating rate-limiting in admittance-controlled pHRI systems as a safety mechanism.

Suturing is required in almost all surgeries but it is challenging to perform with surgical robots due to limited vision and/or haptic feedback. To tackle this problem, we present an autonomous suturing framework that encompasses a novel needle path planner, as well as an accurate needle pose estimator and a six degrees-of-freedom controller. A novel needle grasper is developed that enables needle pose estimation both inside and outside the tissue. The framework was evaluated experimentally using the Raven IV surgical system and important suture parameters were quantified. The experiment results confirmed a needle pose estimation accuracy of°in orientation across all directions. Moreover, the results revealed that using the proposed framework enabled following the reference needle trajectories with errors of 2.07 mm in position and 4.29°in orientation. These are drastic improvements of more than 10× in position and 5× in orientation compared to the Raven IV kinematic controller. Additionally, the results verified that our framework delivered the desired clinical suture parameters successfully across tissue phantom environments with different mechanical properties and under various needle trajectories. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject18.htmlhttp://bionics.seas.ucla.edu/research/surgeryproject18

The International Space Station (ISS) serves as a research lab for a wide variety of experiments including some that study the biological effects of microgravity and spaceflight using the Rodent Habitat and Microgravity Science Glovebox (MSG). Astronauts train for onboard dissections of rodents following basic training. An alternative approach for conducting these experiments is teleoperation of a robot located on the ISS from earth by a scientist who is proficient in rodent dissection. This pilot study addresses (1) the effects of extreme time delay on skill degradation during Fundamentals of Laparoscopic Surgery (FLS) tasks and rodent dissections using RAVEN II; (2) derivation and testing of rudimentary interaction force estimation; (3) elicitation of design requirements for an onboard dissection robot, RAVEN-S; and (4) simulation of the RAVEN-S prototype design with dissection data. The results indicate that the tasks' completion times increased by a factor of up to 9 for a 3 s time delay while performing manipulation and cutting tasks (FLS model) and by a factor of up to 3 for a 0.75 s time delay during mouse dissection tasks (animal model). Average robot forces/torques of 14N/0.1Nm (peak 90N/0.75Nm) were measured along with average linear/angular velocities of 0.02m/s/4rad/s (peak 0.1m/s/40rad/s) during dissection. A triangular configuration of three arms with respect to the operation site showed the best configuration given the MSG geometry and the dissection tasks. In conclusion, the results confirm the feasibility of utilizing a surgically-inspired RAVEN-S robot for teleoperated rodent dissection for successful completion of the predefined tasks in the presence of communications time delay between the ISS and ground control.

In this article, we present a novel stochastic algorithm called simultaneous sensor calibration and deformation estimation (SCADE) to address the problem of modeling deformation behavior of a generic continuum manipulator (CM) in free and obstructed environments. In SCADE, using a novel mathematical formulation, we introduce a priori model-independent filtering algorithm to fuse the continuous and inaccurate measurements of an embedded sensor (e.g., magnetic or piezoelectric sensors) with an intermittent but accurate data of an external imaging system (e.g., optical trackers or cameras). The main motivation of this article is the crucial need of obtaining an accurate shape/position estimation of a CM utilized in a surgical intervention. In these robotic procedures, the CM is typically equipped with an embedded sensing unit (ESU) while an external imaging modality (e.g., ultrasound or a fluoroscopy machine) is also available in the surgical site. The results of two different set of prior experiments in free and obstructed environments were used to evaluate the efficacy of SCADE algorithm. The experiments were performed with a CM specifically designed for orthopaedic interventions equipped with an inaccurate Fiber Bragg Grating (FBG) ESU and overhead camera. The results demonstrated the successful performance of the SCADE algorithm in simultaneous estimation of unknown deformation behavior of the utilized unmodeled CM together with realizing the time-varying drift of the poor-calibrated FBG sensing unit. Moreover, the results showed the phenomenal out-performance of the SCADE algorithm in estimation of the CM's tip position as compared to FBG-based position estimations.

Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated that the learning from demonstration algorithm can boost the learning policy by initializing the predicted dynamics with given demonstrations. Furthermore, the learning from demonstration algorithm is implemented on a Raven IV surgical robotic system and successfully demonstrated feasibility of the proposed algorithm using an experimental approach. This study is part of a profound vision in which the role of a surgeon will be redefined as a pure decision maker whereas the vast majority of the manipulation will be conducted autonomously by a surgical robotic system. A supplementary video can be found at: http://bionics.seas.ucla.edu/research/surgeryproject17.html.

In autonomous suturing with a surgical robot, needle shape, diameter, and path are critical parameters that directly affect suture depth and tissue trauma. This paper presents an optimization-based approach to specify these parameters. Given clinical suturing guidelines, a kinematic model of needle-tissue interaction was developed to quantify suture parameters and constraints. The model was further used to formulate constant curvature needle path planning as a nonlinear optimization problem. The inputs of the optimization include the tissue geometry, surgeon defined entry/exit points, and optimization weighting factors. The outputs are the needle geometry and suggested path. Off-line simulations were used to evaluate the accuracy and performance of the proposed model, and to determine optimized needle geometry and path for several clinically relevant input sets. The output and the optimization results were confirmed experimentally with the Raven II surgical system. The proposed needle path planning algorithm guarantees minimal tissue trauma and complies with a wide range of suturing requirements.

Minimally Invasive Surgery (MIS) can reduce iatrogenic injury and decrease the possibility of surgical complications. This paper presents a novel flexible and bendable endoscopic device, “Roboscope”, which delivers two instruments, two miniature scanning fiber endoscopes, and a suction/irrigation port to the operation site through a single portal. Compared with existing bendable and steerable robotic surgical systems, Roboscope provides two bending degrees of freedom for its outer sheath and two insertion degrees of freedom, while simultaneously delivering two instruments and two endoscopes to the surgical site. Each bending axis and insertion freedom of Roboscope is independently controllable via an external actuation pack. Surgical tools can be changed without retracting the robot arm. This paper presents the design of the Roboscope mechanical system, electrical system, and control and software systems, design requirements and prototyping validation as well as analysis of Roboscope workspece.

Reach-to-grasp movements are widely observed in activities of daily living, particularly in tool manipulations. In order to reduce the complexity in redundancy resolution and facilitate upper-limb exoskeleton control in reach-to-grasp tasks, we studied joint coordination in the human arm during such movements. Experimental data were collected on reach-to-grasp movements in a 3-dimensional (3D) workspace for cylinder targets of different positions and grasping orientations. For comparison, reaching movements toward the same targets are also recorded. In the kinematic analysis, the redundant degree of freedom in human arm is represented by the swivel angle. The four grasping-relevant degrees of freedom (GR-DOFs), including the swivel angle and the three wrist joints, behave differently in reach-to-grasp movements comparing to how they behave in reaching movements. The ratio of active motion range (R-AMR) is proposed for quantitatively comparison the task-relevance of the GR-DOFs. Analysis on the R-AMR values shows that the task-relevant GR-DOFs are more actively used, while the task-irrelevant joints are left uncontrolled and maintain their neutral positions. Among the task-relevant GR-DOFs, the smaller joints (micro-structure) are more actively used than the larger joints (macro-structure). The coordination of the task-relevant GR-DOFs is shown to be synergistic. Analysis of the acceleration/deceleration at the GR-DOFs indicates different levels of voluntary control in three phases of the movements. The study of the characteristics of the joint coordination in reach-to-grasp movements provides guide-lines for simplifying the control of the upper limb exoskeleton.

In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed.

According to the seven degrees of freedom (DOFs) human arm model composed of the shoulder, elbow, and wrist joints, positioning of the wrist in space and orientating the palm is a task requiring only six DOFs. Due to this redundancy, a given task can be completed by multiple arm configurations, and there is no unique mathematical solution to the inverse kinematics. The redundancy of a wearable robotic system (exoskeleton) that interacts with the human is expected to be resolved in the same way as that of the human arm. A unique solution to the system's redundancy was introduced by combining both kinematic and dynamic criteria. The redundancy of the arm is expressed mathematically by defining the swivel angle: the rotation angle of the plane including the upper and lower arm around a virtual axis connecting the shoulder and wrist joints which are fixed in space. Two different swivel angles were generated based on kinematic and dynamic constraints. The kinematic criterion is to maximize the projection of the longest principle axis of the manipulability ellipsoid for the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each two consecutive points along the task space trajectory. These two criteria were then combined linearly with different weight factors for estimating the swivel angle. Post processing of experimental data collected with a motion capturing system indicated that by using the proposed synthesis of redundancy resolution criteria, the error between the predicted swivel angle and the actual swivel angle adopted by the motor control system was less then five degrees. This result outperformed the prediction based on a single criteria.

Surgical procedures are traditionally performed by two or more surgeons along with staff nurses. One surgeon serves as the primary surgeon and the other serves as his/her assistant. Surgical robotics have redefined the dynamics in which the two surgeons interact with each other and with the surgical site. Raven IV is a new generation of the surgical robot system having four articulated robotic arms in a spherical configuration, each holding an articulated surgical tool. The system allows two surgeons to teleoperate the Raven IV collaboratively from two remote sites. The current research effort aims to configure the link architecture of each robotic arm, along with the position (port placement) and orientation of the Raven IV with respect to the patient, in order to optimize the common workspace reachable by all four robotic arms. The simulation results indicate that tilting the base of the robotic arms in the range of -20 to 20 deg while moving the ports closer together up to 50 mm apart leads to a preferred circular shape of the common workspace with an isotropy value of 0.5. A carefully configured system with multiple surgical robotic arms will enhance the interactive performance of the two surgeons.

Despite the great diversity of teleoperator designs and applications, their underlying control systems have many similarities. These similarities can be exploited to enable inter-operability between heterogeneous systems. We have developed a network data specification, the Interoperable Telerobotics Protocol, that can be used for Internet based control of a wide range of teleoperators. In this work we test interoperable telerobotics on the global Internet, focusing on the telesurgery application domain. Fourteen globally dispersed telerobotic master and slave systems were connected in thirty trials in one twenty four hour period. Users performed common manipulation tasks to demonstrate effective master-slave operation. With twenty eight (93%) successful, unique connections the results show a high potential for standardizing telerobotic operation. Furthermore, new paradigms for telesurgical operation and training are presented, including a networked surgery trainer and upper-limb exoskeleton control of micro-manipulators.

In the area of surgical robotics no standard means of performance evaluation has been established. Thousands of surgeons have gone through the SAGES FLS Program, and the psychomotor skill portion of the program is considered the gold standard in laparoscopic skills evaluation. This research describes the use of the FLS block transfer task to evaluate the performance of both surgeons and non-surgeons teleoperating under different time delay conditions on the University of Washington RAVEN Surgical Robot. Time delays of 0 ms, 250 ms, and 500 ms were used and a statistically significant difference in mean block transfer time as well as mean tool tip path length were shown. For this task no significant difference was shown between the surgeon and non-surgeon groups. Clearly surgeon input and feedback is key to surgical robotic system development, but this result implies that non-surgeon subjects can be tested for simple usability evaluations.

In order to enable robotic surgery without human assistance, a means must be developed to change tools. As part of the larger Trauma Pod Project, we developed the Tool Rack Subsystem - an automated tool rack capable of holding, accepting, and dispensing up to 14 tools for the da Vinci surgical robot. Borrowing some techniques from industrial automation, we developed a robust system capable of presenting any stored tool in 700ms or less. Tools are positively retained in a sterilizable carousel in a compliant manner designed to accomodate misalignment during tool exchange. RFID equipment is integrated into the system and the tools so that tools can be inventoried and presented by function or serial number instead of rack position. The resulting device has completed testing and integration into the Trauma Pod system and met all its design requirements.

The exoskeleton robot, serving as an assistive device worn by the human (orthotic), functions as a human-amplifier. Setting the human machine interface (HMI) at the neuro-muscular level may lead to seamless integration and an intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a myoprocessor. It is a model of the human muscle, running in real-time and in parallel to the physiological muscle, that predicts joint torque as a function of the joint kinematics and neural activation levels. The study is focused on developing a myoprocessor based on the Hill phenomenological muscle model. Genetic algorithms were used to optimize model internal parameters using an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufficiently robust for further integration into the exoskeleton control system.

Advances in surgical technology allow physicians to more effectively provide care to their patients. Minimally invasive surgery (MIS) has revolutionized the way a significant number of procedures are performed. Advances in technology have led to the fusion of MIS techniques and robotic devices; however, such systems are currently large and cumbersome. By optimizing a spherical mechanism based on in-vivo data collected during MIS procedures, this paper focuses on a bottom-up approach in developing a new class of surgical robot arms. The spherical mechanism is a rotational manipulator with all axes intersecting at the center of the sphere. Locating the rotational center of the mechanism at the MIS port makes this class of mechanism a suitable candidate for the first two links of a surgical robot for both minimally invasive and open surgery. For optimizing the mechanism structure, the forward and inverse kinematics, as well as the Jacobian matrix, were derived. Using the Jacobian, mechanism isotropy was considered as the performance metric. The dexterous workspace (DWS) is defined as a high dexterity region defined by a right circular cone with a vertex angel of 60/spl deg/ in which 95% of the tool motions are contained based on in-vivo measurements. The extended dexterous workspace (EDWS) is defined as the workspace required to reach the entire abdominal cavity with MIS instruments and defined by a cone with an elliptical cross section created by two orthogonal vertex angels of 60/spl deg/ and 90/spl deg/. Optimization across both the DWS and a superset of the EDWS led to a mechanism configuration with link length angles of 74/spl deg/ and 60/spl deg/ that maximizes kinematic performance and compactness. The workspace of this design covers the entire EDWS and is the optimal design for the next generation of surgical manipulator. By directly applying in-vivo experimental data from MIS in order to optimize the spherical manipulator a design that maximizes performance and minimizes size has been developed. A pair of prototype manipulators is developed based on these results.

Minimally invasive surgery involves a multidimensional series of tasks requiring a synthesis between visual information and the kinematics and dynamics of the surgical tools. Analysis of these sources of information is a key step in mastering MIS, but may also be used to define objective criteria for characterizing surgical performance. The BlueDRAGON is a new system for acquiring the kinematics and dynamics of two endoscopic tools synchronized with the visual view of the surgical scene. It includes two four-bar passive mechanisms equipped with position and force torque sensors for measuring the positions and orientations of the two endoscopic tools along with the forces and torques (F/T) applied by the surgeon's hands. The methodology of decomposing the surgical task is based on a fully connected, 28 finite-states Markov model where each states corresponded to a fundamental tool/tissue interaction based on the tool kinematics and associated with unique F/T signatures. The experimental protocol includes seven MIS tasks performed on an animal model by 30 surgeons at different levels of their residency training including expert surgeons. From the preliminary analysis of these data, the major differences between residents at different skill levels are discussed. Systems like surgical robots or virtual reality simulators that inherently measure the kinematics and dynamics of the surgical tool may benefit from inclusion of the proposed methodology for the analysis of efficacy and objective evaluation of surgical skills during training.

This paper studies the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The human-machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked by the human operator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental results indicate the feasibility of an EMG-based power exoskeleton system as an integrated human-machine system using high-level neurological signals.