The task of accurate teleoperation of a large workspace industrial manipulator using a small haptic device having a physically limited workspace still a challenge. This task should be handled carefully without disturbing the user perception of continuously teleoperating the manipulator movement in a natural and precise way. This paper presents the development of a master/slave teleoperation system based on a new workspace spanning technique that integrate position control and a modified rate control methods to solve the problem of workspace mapping of two kinematically dissimilar robots. The proposed technique referred to as Positioning with Modified Rate Control is simple yet efficient and overcomes most of the drawbacks of other techniques. It depends on switching between rate control with constant speed for coarse motion and position control for accurate movements. The developed system consists of a phantom premium haptic device as a master and RV-2AJ Mitsubishi robot arm as a slave. Experimental results demonstrate that the operator can easily navigate through the whole workspace of an industrial robot using a small haptic device while achieving accurate positioning. The developed system proved experimentally to have good tracing characteristics in the fine manipulation mode. Besides, a 6 DOF force/torque sensor is attached to the end effector and tested for contact with different stiffness materials.

Patients with low back pain are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Model Predictive Control (iMPC) problem. The standard Model Predictive Control (MPC) structure includes constraints, therefore, it allows us to model the physiological constraints of motor control. We devised an iMPC algorithm to solve iMPC problems with experimentally collected output trajectories. We used experimental data of one healthy subject during a seated balance test that used a physical human-robot interaction. Results show that the estimated MPC weights reflected the task instructions given to the subject and yielded an acceptable goodness of fit. The iMPC solution suggests that the subject's control intent was dominated by minimizing the squared sum of a combination of the upper-body and lower-body angles and velocities.

The control intent is a strategy which a human subject follows to accomplish a certain motor control task such as controlling the trunk movement. The problem we address is how control theoretic approaches can capture such a strategy taking into account the physiological constraints and which approach is better.We present such an analysis, so-called intent-inferring. The control intent can be inferred by estimating the cost function that is minimized by a control system. We propose an inverse model predictive control (iMPC) algorithm to infer the control intent. We solve the iMPC problem for an illustrative example to evaluate the algorithm and to compare against inverse linear quadratic regulator (iLQR) approach. The simulated results show that our algorithm can recover the true cost function weights, and outperform the iLQR approach for the illustrative example.

This paper presents the experimental implementation of a teleoperated endoscopic surgical manipulator system that uses PHANTOM Omni haptic device as the master. The 4-DOF, 2-PUU 2-PUS, endoscopic surgical parallel manipulator design is carried out using screw theory and Parallel virtual chain methodology to have larger bending angles and workspace volume. The master and slave devices of the teleoperation system are dissimilar in their kinematics and workspace volumes. A workspace mapping technique is implemented based on Position with Modied Rate Control to navigate through the slave workspace without annoying the user. To control the motion of the slave robot, a PID controller is used. The experimental results show the feasibility of the teleoperation surgical system using the 4-DOF parallel manipulator. Also, they indicate the efficiency of the implemented mapping technique and the designed controller to span the slave workspace with high dexterity and good tracking which allows the surgeon to perform the operation with high accuracy.

Estimating many parameters of biomechanical systems with limited data may achieve good fit but may also increase 95% confidence intervals in parameter estimates. This results in poor identifiability in the estimation problem. Therefore, we propose a novel method to select sensitive biomechanical model parameters that should be estimated, while fixing the remaining parameters to values obtained from preliminary estimation. Our method relies on identifying the parameters to which the measurement output is most sensitive. The proposed method is based on the Fisher information matrix (FIM). It was compared against the nonlinear least absolute shrinkage and selection operator (LASSO) method to guide modelers on the pros and cons of our FIM method. We present an application identifying a biomechanical parametric model of a head position-tracking task for ten human subjects. Using measured data, our method (1) reduced model complexity by only requiring five out of twelve parameters to be estimated, (2) significantly reduced parameter 95% confidence intervals by up to 89% of the original confidence interval, (3) maintained goodness of fit measured by variance accounted for (VAF) at 82%, (4) reduced computation time, where our FIM method was 164 times faster than the LASSO method, and (5) selected similar sensitive parameters to the LASSO method, where three out of five selected sensitive parameters were shared by FIM and LASSO methods.