In this paper, a method for 3D non-rigid motion estimation of a surface using an RGB-D camera in eye-in-hand configuration is presented. The eye-in-hand configuration eliminates errors typically associated with camera-end-effector calibration, and is thus desirable for task on moving surfaces such as bioprinting. However, its implementation is challenging since camera and surface of interest are moving, making mesh-based approaches unsuitable. Thus, the proposed method operates directly on point clouds, benefiting from accurate and simplified data processing. A point cloud contains both intensity and depth data, with the former used to estimate in-plane deformation and the latter to compute full 3D deformation. Surface deformation is modeled via a Thin Plate Spline model. The method accuracy is assessed at 0.1 mm accuracy in simulated datasets, rendering it suitable for precision tasks, and its feasibility is validated experimentally on a moving platform that deforms at a rate of 0.8 Hz with a 4 mm in-plane amplitude and a 20 mm elevation amplitude.

This paper introduces a 2-DOF spatial remote center of motion (RCM) tensegrity mechanism, based on a double parallelogram system, dedicated for percutaneous needle insertion. The originality of this mechanism is its ability to be reconfigured and its capacity to perform a decoupled modulation of its stiffness in an asynchronous way. To do so, an analytical stiffness model of the robot is established, and a control methodology is proposed. A prototype of the robot is developed and assessed experimentally. The position tracking is evaluated using a 6-DOF magnetic tracker sensor showing a root mean square error less than 0.8° in both directions of the needle guide.

Most of existing works on the tubes design optimization of concentric-tube robot (CTR) do not include the elastic stability in the optimization criteria. The only work which formulates the elastic stability in the objective function is based on scalarization method which is used in existing multi-objective design optimization. The objective function is formed by a set of weighted objective functions. The selection of the weights is crucial as the optimization results are greatly affected by them and could be misleading if these weights are improperly chosen. As an alternative optimization technique, we use Pareto grid-searching method to avoid this problem and allow a straightforward interpretation of the results following the selection criteria for the parameters to be optimized. This paper shows a three-tube CTR design based on Pareto grid-searching method in order to optimize the reachability and elastic stability of the CTR within a specific curvature range dedicated to the deep anterior brain tumor removal surgery.

This paper introduces a new robust 3D ultrasound needle detection approach integrated in a 3D needle steering system associated to a real-time path planning. The robustness of an existing algorithm is improved by limiting the needle detection to a curvilinear region of interest (ROI) using a novel mechanical-based prediction model. This linear model is also used in a Kalman filter to reduce detection noise and reject false detections. These two improvements drastically increase quality of our feedback. Finally, the 3D needle steering system is able to reach a target in phantoms with a maximal error of 0.8 mm without obstacle and 1.6 mm with obstacle.

In this paper, the design of a new variable stiffness spherical joint for MR-compatible robotics is presented. It is based on the use of prestressed cable-driven mechanisms in singular configurations to provide large stiffness variation ranges, including zero stiffness configuration as required by the medical context. An original implementation is proposed, with a prestress adjustment system using pneumatic energy and taking advantage of multimaterial additive manufacturing. The proposed component combines compactness, MR-compatibility and is lightweight. The system is evaluated on a dedicated experimental setup with validation of the expected behavior, with in particular a very large achievable range of stiffnesses. The approach is effective for the design of such device and constitutes a novel solution for the design of variable stiffness devices with complex motions.

Needle placement errors can mitigate the effectiveness of the diagnosis or the therapy, sometimes with catastrophic outcomes. Previous design of a simplified model for needle deflection estimation was motivated by the clinical constraints of ARCS (Abdomino-pelvic Robotic-driven slightly flexible needle insertion performed in CT/MRI-guided Scenario). We present in this work, the validation results for the needle deflection prediction model. Its robustness is evaluated under an unknown context such as a different robotic platform, facing uncertainties conditions not conceived previously in the model's confection. In addition, the work presents the development and validation experiments of an adaptive path planner that uses the model as predictor's strategy. It provides pre-operative planning assistance, as well as intra-operative decision-making support. The experiments results showed average error around 1mm for the pre-operative planning and the intra-operative replanning approach showed to be very robust to correct the initial predictions, showing average error smaller than 1 mm.

In this paper we propose a modeling method for the interaction impedance of a remote soft tissue that contains quasi-periodic physiological motion disturbance. Through this study, it is shown that the interaction with such environment is not passive and its influence should be considered in the teleoperator design. The interaction impedance depends not only on the soft tissue impedance but also on the relationship between the robotic tool motion and the soft tissue motion disturbance. An illustrative case study is presented to demonstrate how to analyze the environment interaction impedance in real application.

Many medical applications can benefit from the new technology of concentric-tube robot (CTR) due to its miniature size, superior steerability, and controllability of the end tool. However, the kinematic modeling of CTR is challenging because of complicated physical phenomena caused by the elasticity interaction between tubes. Existing control methods of CTR are based on inverse kinematics calculation and hence the control performance largely relies on the accuracy of kinematics model used. In this work, we propose a new control method from the actuator level and show that the control design of actuator input in task-space with approximate Jacobian matrix provides more flexibility and robustness in handling inaccuracy in kinematics model. It is shown through simulation study that the proposed control method presents better performance compared with traditional inverse kinematics based control method in face of kinematics inaccuracy.

In the medical field, several surgical simulators and training platforms have been developed to help novice surgeons improve their surgical skills, as well as perform preoperative planning. In this paper, a haptic platform for surgical needle insertion training gestures is presented. Instead of relying on active actuators, an alternative solution, consisting in passive brakes based on Electro-Rheological (ER) fluids, is proposed, to provide a safe and realistic physical feedback to the physician. This platform generates a passive repulsive force against the user's movement, providing him/her a physical stimulus and, thus, a realistic haptic feedback. The goal of this project is to prove the reliability of ERF-based brakes to simulate the physical resistance of soft tissues against the movement of a surgical needle, in order to train unskilled practitioners in different scenarios. To achieve this objective, a prototype has been built, its kinematic model has been obtained and experimentally validated. The modelling, the bandwidth analysis and the force control scheme of the platform are also presented.

Tele-surgery has been more and more popular in robotassisted medical intervention. Most existing teleoperation architectures for medical applications adopt 2-channel architectures. The 2-channel architectures have been evaluated in literature and it is shown that some architectures, e.g. position-force (P-F), are able to provide the surgeon a reliable haptic sense of the working environment (transparency). However, stability of these P-F architecture is still a considerable concern especially when physiological disturbances exist in the remote environment. P-PF architecture is proved to provide a convenient alternative. With one more channel 3-channel teleoperation architectures present promising options due to their augmented design flexibility. This paper evaluates stability and transparency of general 3-channel bilateral teleoperation control architectures and provides a design framework guidelines to improve the architectures' stability robustness and optimize the transparency. Simulation evaluations are provided to illustrate how the optimal 3-channel teleoperation architecture is chosen for medical applications given their dedicated requirements.

This paper presents a semi-automatic system for robotically assisted 2D needle steering that uses duty-cycling to perform insertions with arcs of adjustable curvature radius. It combines image feedback manually provided by an operator with an adaptive path planning strategy to compensate for system uncertainties and changes in the workspace during the procedure. Experimental results are presented to validate the proposed platform.

Controlling the interaction between the robot and living soft tissues has became an important issue as the number of robots inside the operating room increases. Many research works have been done in order to control this interaction. Nowadays, researches are running in force control for helping surgeons in medical procedures such as motion compensation in beating heart surgeries and tele-operation systems with haptic feedback. The viscoelasticity property of the interaction between organ tissue and robotic instrument further complicates the force control design which is much easier in other applications by assuming the interaction model to be elastic (industry, stiff object manipulation, etc.). In order to increase the performance of a model based force control, this work presents a force control scheme using Active Observer (AOB) based on a viscoelastic interaction model. The control scheme has shown to be stable through theoretical analysis and its performance was evaluated and compared with a control scheme based on a classical elastic model through experiments, showing that a more realistic model can increases the performance of the force control.

An analysis of stability and transparency of a force feedback teleoperation system for cutting-edge robotic surgery is presented. Previous works in teleoperated robotic surgery do not consider the real behavior of the environment, which was supposed to be only elastic. However, new surgical procedures in which the environment dynamics plays a crucial role start emerging as a result of technological progress. In robotic assisted beating-heart surgery, for instance, the dynamics of the contact between surgical tools and soft tissues has an impact not only in the performance of the force control task but also in the performance of the teleoperation control scheme in terms of transparency and stability. Therefore, a more realistic description of the environment has to be adopted in order to safely operate during robot-patient interaction. For this purpose, a viscoelastic contact model is introduced into the bilateral teleoperation scheme, and a performance study is provided. The obtained results show the advantages of the selected approach when targeting teleoperated surgical interventions in which the interaction dynamics has become a significant issue.

In this paper we propose a novel approach for interactive manipulation involving a human and a humanoid. The interaction is represented by means of the relative configuration between the human's and the robot's hands. Based on this principle and a set of mathematical tools also proposed in the paper, a large set of tasks can be represented intuitively. We also introduce the concept of simultaneous handling using mirrored movements, where the human controls the robot and simultaneously interacts with it by means of a common manipulated object. Illustrative experiments are performed to validate the proposed techniques.

In this paper, we propose a novel anti-phase tremor compensation method using surface electromyography (SEMG) and accelerometer (ACC). The usefulness of the SEMG signal is that it precedes the generated joint movement by 20 100 ms (electromechanical delay, EMD). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by Functional Electrical Stimulation (FES). This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. Specifically, what is proposed in this paper is algorithm to an estimate the EMD and to determine when to start/stop the FES such that anti-phase tremor cancellation. Experimental result from one Essential Tremor patient show 57% reduction in tremor power as measured by the ACC.

This paper presents an adaptive approach for 2D motion planning of steerable needles. It combines duty-cycled rotation of the needle with the classic Rapidly-Exploring Random Tree (RRT) algorithm to obtain fast calculation of feasible trajectories. The motion planning is used intraoperatively at each cycle to compensate for system uncertainties and perturbations. Simulation results demonstrate the performance of the proposed motion planner on a workspace based on ultrasound images.

In this paper, a strategy to attenuate tremor based on co-contraction of antagonist muscles using Functional Electrical Stimulation (FES) is fully presented. Both methods to track tremor features in real-time, while filtering voluntary motion, and to identify a suitable joint model are described. Using this information, the stimulation controller modulates joint stiffness based on tremor intensity, while preventing the generation of undesirable joint torque. An experimental evaluation of the system, which confirmed the effectiveness of the approach, is also presented.

A robust cascade strategy combining an outer position predictive control loop and an inner H∞pressure control loop is proposed and tested on an electropneumatic testbed for parallel robotic applications. Two types of cylinders are tested, the standard double acting cylinder and the rodless one. A position/pressure difference (or force) strategy is developed and implemented. As the behavior of the nonlinear cylinders is nonlinear, a feedback linearization strategy is adopted. A Generalized Predictive Controller (GPC) is synthesized for the position outer loop and a constrained LMI based H∞controller is synthesized for the pressure inner loop. Experimental results show the feasibility of the control strategies and good performances in terms of robustness and dynamic tracking.

In this paper, the regulation problem of rigid-link electrically-driven (RLED) robotic manipulators with uncertain kinematics and dynamics is addressed. A task-space Saturated-Proportional Integral and Differential (SP-ID) based control approach is proposed using backstepping technique to deal with the uncertainties in actuator dynamics, robot dynamics and kinematics. The proposed method is structurally simple and easy for implementation. Sufficient conditions for choosing the feedback gains, approximate Jacobian matrix and motor torque constant matrix are provided to guarantee system stability. Simulation results demonstrate the effectiveness of the proposed approach.

In the context of minimally invasive cardiac surgery, robotic assistance has significantly helped surgeons to overcome difficulties related to the minimally invasive procedure. Recently, techniques have been proposed for active canceling the beating heart motion for improving the accuracy of the surgical gestures. In this scenario, computer vision techniques can be applied for estimating the heart motion based solely on natural structures on the heart surface. However, visual tracking is complicated by the particular lighting conditions and clutter (smoke, liquids, etc) during surgery. Another challenging problem are the occasional occlusions by surgical instruments. In order to overcome these problems, we exploit the quasi-periodicity of the beating heart motion for increasing the robustness of the visual tracking task. In this paper, a novel time-varying dual Fourier series for modeling the quasi-periodic beating heart motion is proposed. For estimating the series parameters, an Extended Kalman Filter (EKF) is used. The proposed method is applied in a visual tracking task for bridging tracking disturbances and automatically reestablish tracking in cases of occlusions. The efficiency of the prediction method and the sensible improvements in the visual tracking task are demonstrated through in vivo experiments.

International audience

The paper presents a control architecture for robotic-assisted surgery in the presence of physiological motions. Dynamic and kinematic models, operational space, computed torque, discrete state space and stochastic design are addressed in the control. Inner loops are based on position and velocity signals and outer loops have force measurements. Two active observers (AOBs) are introduced for force control and motion compensation. The first AOB is responsible for model-reference adaptive control to guarantee a desired closed loop dynamics for the force. The second AOB performs control actions to compensate physiological motions. Such motions are described by a second-order stochastic equation, without apriori knowledge of signal characteristics. Simulation results are presented for sinusoidal and non-sinusoidal motions, highlighting merits of the approach.

This paper concerns the whole physiological parameters identification of a musculoskeletal model of a human subject. The patient is equipped with an implanted Functional Electrical Stimulation (FES) system as part of the SUAW's European project. The biomechanical model represents the knee and its associated muscles. The identification protocol is noninvasive and based on the in-vivo experimental data acquisition of a Spinal Cord Injured (SCI) patient. However, the human noninvasive identification poses problems of inaccessibility to some data. The identification procedure consists of several steps, in order to identify: the anthropometrical parameters, the geometrical parameters, the joint mechanical parameters, the force-length relationship and the recruitment function. Up to now, only the quadriceps muscle is considered with the knee joint in the identification procedure. A cross-validation has been done using data set not used during the identification process. The identified model shows a satisfactory response comparing to the measured knee response, which is obtained by stimulating the quadriceps through the implanted FES system. In this work, knee model-parameters of the implanted subject were identified successfully using the noninvasive identification procedure.

This paper describes an algorithm to estimate tremor and voluntary motion from measured motion data. Estimation is performed by means of an extended Kalman filter (EKF), which also estimates tremor parameters. Comparison of the proposed method with techniques described in the literature are conducted with two experimental data sets from tremor patients performing the same task, drawing a spiral. The presented algorithm may be directly applied in real-time pathological tremor compensation systems.

In this paper, a strategy for pathological tremor compensation based on co-contraction of antagonist muscles induced by Functional Electrical Stimulation (FES) is presented. Although one of the simplest alternatives to apply FES for reducing the effects of tremor, the contribution of different co- contraction levels for joint motion and impedance must be accurately estimated, specially since tremor itself is highly time-varying. In this work, a detailed musculoskeletalmodel of the human wrist actuated by flexor and extensor muscles is used for this purpose. The model takes into account different properties that affect muscle dynamics, such as proprioceptive feed- back and combined natural and artificial activation. The model, analysis of stiffness modulation due to FES-controlled co-contraction and simulation results are presented in the paper.

This paper presents a predictive force control approach to compensate for the physiological motion induced by both respiratory and heart beating motions during cardiac surgery. It focuses on the design and implementation of the control algorithm in the context of robotized minimally invasive surgery. The controller is based on a linear predictive control loop using the force information applied on the heart by the instrument. Experimental evaluation highlights the performance of the algorithm for compensating 3D physiological motion.

EMG-to-force estimation for voluntary muscle contraction has many applications in human-machine interaction, motion analysis, and rehabilitation robotics for prosthetic limbs or exoskeletons. EMG-based model can account for a subject's individual activation patterns to estimate muscle force. For the estimation, so-called Hill-type model has been used in most of the cases. It already has shown its promising performance, but it is still known as a phenomenological model considering only macroscopic physiology. We have already developed the physiological based muscle model for the use of functional electrical stimulation (FES) which can render the myoelectrical property also in microscopic scale. In this paper we discuss EMG-to-force estimation based on this full physiological based muscle model in voluntary contraction. In addition to Hill macroscopic structure, a microscopic physiology originally designed by Huxley is integrated. It has significant meaning to realize the same kind of EMG-to-force estimation with a physiological based model not with a phenomenological model, because it brings the understanding of the internal biophysical dynamics and new insights about neuromuscular activations. Using same EMG data of isometric muscle contraction, the force estimation results are shown by classical approach and new physiological based approach. Its interpretation is also discussed.

This paper presents an optimal design of a parallel manipulator aiming to perform pick-and-place operations at high speed and high acceleration. After reviewing existing architectures of high-speed and high-acceleration parallel manipulators, a new design of a 4-DOF parallel manipulator is presented, with an articulated traveling plate, which is free of internal singularities and is able to achieve high performances. The kinematic and simplified, but realistic, dynamic models are derived and validated on a manipulator prototype. Experimental tests show that this design is able to perform beyond the high targets, i.e., it reaches a speed of 5.5 m/s and an acceleration of 165 m/s2. The experimental prototype was further optimized on the basis of kinematic and dynamic criteria. Once the motors, gear ratio, and several link lengths are determined, a modified design of the articulated traveling plate is proposed in order to reach a better dynamic equilibrium among the four legs of the manipulator. The obtained design is the basis of a commercial product offering the shortest cycle times among all robots available in today's market.

A model-based FES would be very helpful for the adaptive movement synthesis of spinal-cord-injured patients. For the fulfillment, we need a precise skeletal muscle model to predict the force of each muscle. Thus, we have to estimate many unknown parameters in the nonlinear muscle system. The identification process is essential for the realistic force prediction. We previously proposed a mathematical muscle model of skeletal muscle which describes the complex physiological system of skeletal muscle based on the macroscopic Hill-Maxwell and microscopic Huxley concepts. It has an original skeletal muscle model to enable consideration for the muscular masses and the viscous frictions caused by the muscle-tendon complex. In this paper, we present an experimental identification method of biomechanical parameters using Sigma-Point Kalman Filter applied to the nonlinear skeletal muscle model. Result of the identification shows its effective performance. The evaluation is provided by comparing the estimated isometric force with experimental data with the stimulation of the rabbit medial gastrocnemius muscle. This approach has the advantage of fast and robust computation, that can be implemented for online application of FES control.

Currently there is a lack of objective clinical diagnosis and classification of tremor is difficult when it is subtle. Thus in previous work, a sensing system has been developed to quantify pathological tremor in human upper limb. In this paper, a Kalman filter algorithm to fuse information from accelerometers and surface electromyography is proposed. As the ground truth, an optical motion tracking system will be utilized. Then two sensor fusion algorithms based on Kalman filter are formulated to estimate the joint angle of the limb from the reading of accelerometers and surface EMG. Initial results using tremor data from two Parkinson's disease patients show promising future in this sensor fusion. The sensing system and the algorithms proposed are useful for actively compensating the tremor and helping the clinicians in tremor diagnostics.

The work presented in this paper concerns the synthesis of Functional Electrical Stimulation (FES) patterns to generate movements of paralysed limbs for spinal cord injured patients. We propose an approach based on a nonlinear optimization formulation that may encounter physiological and technological constraints. The study considers a biomechanical knee model and the associated agonist/antagonist muscles. The goal of this method is to synthesize optimal patterns which minimize the muscular activities and/or tracking trajectory errors in order to reduce the muscular fatigue while achieving a desired movement. Different tests have been performed and the results compared with regard to the energetic balance. The approach is illustrated in simulation with: 1) sinusoidal desired knee joint trajectory, 2) optimal reference knee joint trajectory and 3) without explicit reference knee joint trajectory. The simulations have been performed with model parameters estimated from real subject data. We show that the trajectory tracking presents high energy consumption which demonstrates the inappropriateness of classical robotics methods for musculoskeletal system. Instead, minimization of muscle activation only gives better results with regard to energy consumption, still with a reasonnable trajectory tracking error.

This paper proposes a new approach to compensate the physiological motion, induced by respiration and heart beating, for robotized minimally invasive cardiac surgery. The control algorithm, based on a linear predictive control, uses the effort information applied on the heart by the instrument.

With the advent of new applications in cardiac robotic-assisted minimally invasive surgery (MIS), a demand for the design of efficient motion compensation systems was created. In this context, vision-based techniques seem to be a practical way to retrieve the motion of the beating heart since they do not require the introduction of additional sensors in the limited workspace. In this paper, we propose an efficient method for tracking the heart surface which incorporates two novelties. The first is a thin-plate splines (TPS) parametric model for the heart surface deformation that allows us to better track regions of the heart surface with little texture information which undergo large non-rigid deformations. The second novelty is the incorporation of a performing illumination compensation algorithm to cope with arbitrary illumination changes and increase tracking robustness.We also extend this framework for 3D tracking, to enable full compensation of the heart motion. Extensive experiments conducted onin-vivoandex-vivoheart images attest the notable performance of the algorithm.

The main challenge that we face when applying functional electrical stimulation (FES) to paralyzed lower limbs is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. FES is used to excite paralyzed muscles that are under lesions and consequently no more controlled by paraplegic patients. We aimed in this study to compute the needed patterns stimulation necessary to perform a desired given motion of the knee joint. We coupled the exact input output feedback linearization with a model predictive controller (MPC). This latter enables us to incorporate explicitly constraints on inputs, outputs and system states. Internal dynamics stability was mathematically proved and MPC performances were compared to a classical pole placement controller in terms of robustness, stability and finite time convergence.

This paper presents the performance of a needle insertion teleoperated system with haptic feedback. Through a haptic device, the surgeon tele-operates the surgical needle, fixed on the remote robot that may evolve in free space or in contact. In free space, the surgeon feels the motion of the robot. During the needle insertion, the puncture of the tissue involves an abrupt and large change in the magnitude of the applied force. To perform and improve the tele-operated task, an on-line transparency improvement is achieved using the environment stiffness estimation. The transparency is improved based on the stability of the haptic feedback teleoperation scheme in which we distinguish between the voluntary and involuntary operator position. The quality of telepresence allows the surgeon to perform and exactly characterize the needle insertion phases without using additional transition detection techniques. To assess the proposed teleoperation scheme, a surgical needle rigidly attached to the robot and ex-vivo tissues are used.

This paper discusses the design of a control system for robotic-assisted surgery with haptic feedback. The operational space control has a position-position teleopearation architecture with the phantom in the loop, enabling telepresence in free-space and contact. The null space control guarantees that surgical kinematic constraints are fulfilled. Both task and posture control run active observers (AOBs) in Cartesian domain, taking into account force, velocity and position signals. Experiments with a D2M2 (direct drive medical manipulator) robot are presented

The main goal when applying functional electrical stimulation (FES) to the paralyzed lower limbs of the paraplegic patients is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. In this paper a closed loop position control of the knee joint actuated by the quadriceps muscle to perform flexion-extension has been presented. The feedback control consists of a model predictive control (MPC) technique which is also known by a receding horizon control or moving horizon control. This controller is applied to a complex physio-mathematical muscle model that is based on a macroscopic Hill and a microscopic Huxley concepts. An MPC constitutes an adequate controller with nonlinear multivariable systems. Furthermore it enables us to incorporate explicitly constraints on inputs, outputs and system states. The controller has shown a robustness against force perturbation and model mismatch as well as high capability of tracking a pre-defined reference trajectory

This paper introduces the dynamic modeling and the identification of Par4, a four-degree-of-freedom parallel manipulator producing Schonflies motions (three translations and one rotation about a fixed axis). First of all, this paper presents how this robot is developed with the goal of reaching very high speed. Indeed, it is an evolution of Delta, H4 and I4 robots architectures: it keeps their advantages while overcoming their drawbacks. Experimentations done with the prototype prove that the robot is able to reach very high accelerations (15 G) and to perform an adept cycle in 0.25 s. In order to improve its dynamic accuracy, a dynamic control could be necessary. Thus, this paper presents the dynamic modeling of the manipulator using a simplified Newton-Euler approach. The originality of this computation is to model the traveling as two separated parts and to determine the dynamic effects applied on each of them. Finally, since a dynamic control requires a good evaluation of dynamic parameters, an experimental dynamic identification is presented

The BRIGIT project (Bone Resection Instrument Guidance by Intelligent Telemanipulator) aims at developing a surgical robot for orthopedic surgery. This robot should be used as a positioner of a guide providing a mechanical support during bone sawing or drilling. The planned position of the guide is obtained after a registration procedure consisting in collecting anatomical landmarks on the surface of the patient's bone. This can be done in a cooperative mode, by grabbing the tool tip, through an appropriate force control, or in a teleoperated mode via a master device. In order to facilitate the installation of the robot in the operating theatre and to improve its performance, a procedure based on interval analysis has been developed to optimize the robot placement with respect to the patient, the surgical staff, and the obstacles of the environment.

Functional electrical stimulation (FES) is used to excite paralysed muscles that would otherwise be uncontrollable by paraplegic patients. Consequently, the patient could recover partially some of lower limb functions improving the cardiovascular system, increasing oxygen uptake and bettering the whole quality of life. In this paper, we apply a control design based on a higher order sliding mode to a complex physio-mathematical muscle model. This model is based on macroscopic Hill and microscopic Huxley concepts. The main goal concerns the prediction of the needed pattern stimulation (current and pulse width), which will extend the overall performances and defer the muscle fatigue as much as possible. The controller is mathematically computed and shown to provide satisfactory stability and tracking errors. Its efficiency is illustrated with the control of the knee joint angle under a co-contraction approach.

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to muscles is interrupted. Functional Electrical Stimulation (FES) applied to the lower motor neurons can replace the lacking signals. A neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of FES. The neuroprosthesic implant allows muscles to be controlled with high accuracy, high selectivity and the repeatability of the muscle's response can be achieved. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. The system is thus insufficient to enhance significantly the daily-life of the patient, nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards the automatic synthesis of the stimulation patterns generating the desired movement and closed loop control. To achieve this goal, some preliminary researches have to be carried out; starting with a specific modeling that can be used in the contest of FES. The main issues concern muscle modeling including FES parameters as inputs, fatigue, the interaction with the skeleton, and the identification of parameters. This paper describes the mathematical modeling of the skeletal muscle.

This paper deals with the use of an original dynamic task/posture decoupling control algorithm that allows a robot to achieve motions under the constraint of moving through a fixed point. This work takes place in the context of minimally invasive surgery where the tool is telemanipulated by the surgeon through a penetration point: the trocar fixed on the patient. The algorithm is based on the dynamic control in the operational space of a redundant robot: the total control torque is decoupled into a task behavior torque and a posture behavior torque. By minimizing the contact force applied to the trocar (or equivalently, by forcing to zero the distance between the instrument passing through the trocar and the current location of the trocar), we compute the posture behavior torque guaranteeing that the trocar constraint is satisfied. Simulation results highlight the performance of this algorithm for various trajectories such as straight lines and circles.

The new generation of implanted neuroprostheses allows muscles to be controlled with fine accuracy, high selectivity and the repeatability of the muscle's response can be achieved. Thus, the closed loop control of such systems becomes possible. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. Nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards closed loop control and automatic synthesis of the stimulation patterns generating the desired movement. To achieve this goal, some preliminary researches have to be carried out, beginning with a specific modelling that can be used in the context of functional electrical stimulation (FES). The main issues concern muscle modelling including the interaction with the skeleton, fatigue, FES parameters as inputs, and the identification of dynamic parameters, and afterwards, the motion synthesis and the closed loop control based on this model. Besides, the scientific approach is the same as in robotics so that the theoretical tools used in the control theory are the same and directly applicable. This paper describes the results obtained in the previous project SUAW and how we attempt, through the new project DEMAR, to enhance the global performances of the system.

This paper deals with the experimental identification of the dynamic parameters of parallel machines. The dynamic parameters are estimated by using the weighted least squares solution of an over determined linear system obtained from the sampling of the dynamic model along a closed loop exciting trajectory. Experimental results are exhibited for the H4 robot, a fully parallel structure providing 3 degrees of freedom (DOF) in translation and 1 DOF in rotation. A comparative study is performed depending on the available measurements, i.e., different sensor locations (motor, end effector).

This paper presents the application of an ellipsoidal method for robust dynamic identification of parallel robots. The robot is modelled with classical Lagrange equation which leads to an inverse dynamic model linear with respect to the parameters. Assuming the error additive on input (motor torque), the problem is expressed in a bounded error context. The ellipsoidal method is applied in a factorised form in order to guarantee numerical stability. Experimental results are exhibited for a fully parallel robot with 4 degrees of freedom.

This paper presents an efficient application of a model based predictive control in parallel mechanisms. A predictive functional control strategy based on a simplified dynamic model is implemented. Experimental results are shown for the H4 robot, a fully parallel structure providing 3 degrees of freedom (dof) in translation and 1 dof in rotation. Predictive functional control, computed torque control and PID control strategies are compared in complex machining tasks trajectories. The tracking performances are enlightened.

This paper presents a novel and safe robotic system for skin harvesting, the first one in reconstructive surgery. It is intended to significantly improve the performance of surgeons who do not regularly perform this operation; the tool, called dermatome, is mounted at the tip of a dedicated robot that precisely controls the pressure on the skin and the harvesting velocity. In this paper, the harvesting task is analyzed and the safety constraints are summarized. Then, the mechanical structure and the functions of the control system are described. Finally, in vivo experimental results on pigs are reported and discussed.

This paper deals with a new control approach for biped robots. The technique is inspired by the prediction capability of human being. Optimal computations over a moving horizon are performed with a set of constraints which is modified online to be adapted to the obstacle-filled environment. Simulation results show the efficiency of the algorithm in the case of a gait on flat terrain and steep stairs.

Deals with a comparison of sensor location and nature in the identification of physical parameters for mechanical systems with lumped elasticities. The identification model is a linear model in relation to a minimal set of parameters. The dynamic parameters are estimated by using the solution of weighted least squares of an over determined linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. An experimental study exhibits the identification results depending on two types of sensors (position, acceleration) and different locations (motor, load).

This paper deals with a path planning algorithm for an anthropomorphic robot that must achieve motions (straight line, circle, helix) under the constraint of passing the forearm through a fixed point. This point simulates the penetration point (trocar) in minimally invasive surgery. It has to remain fixed with respect to the patient's body. The modeling and resolution of the constraint are presented. The geometric model and path planning are proposed. Simulation results validate the algorithm.

Proposes a method to identify the joint stiffness of a robot using a bandpass filter. It is based on moving one axis at a time. The dynamic model reduces to a model which is linear in relation to a minimum set of dynamical parameters which have to be identified. These parameters are estimated using the least squares solution of an over determined linear system obtained from the sampling of the dynamic model along a closed loop tracking trajectory. Conditions for a good data processing before identification are exhibited through practical aspects concerning data sampling and data filtering. An experimental study shows the efficiency of the method with two sets of data depending on motor joint position measurements.

This paper presents a comparison of two methods for robot dynamic identification which include the weighted least squares estimation and the extended Kalman filtering. Comparative experimental results and discussion are presented for a SCARA robot.

The paper presents the results of a comparative study of the respective efficiency of three parametric signal processing methods to detect abrupt spectral changes by means of the detection of abrupt model discontinuities, while they were applied to the very particular case of inspection of change in myoelectric activity of surface electromyograms (EMG). The studied surface electromyograms are those of biceps brachii during a perturbed flexion-extension forearm movement in the horizontal plane. After the description of the experimental device, the position problem is then formally considered, and the different methods used are briefly recalled. Finally, the results observed on a large set of trials are shown to highlight the behaviour of each selected method before concluding on the opportunity to use them to characterize some neuropathies.