Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

The problem of robot drilling presents a significant challenge, due to the comparatively low mechanical stiffness of typical serial industrial robots. This compliance makes the robot deflect due to the cutting forces, resulting in poor hole quality. Recently, functionality for high-bandwidth force control has found its way into industrial robot control systems. This could potentially open up the possibility of robotic drilling systems with improved performance, using only standard systems without costly extra hardware and calibration techniques. In this paper, we present methods and systems for force-controlled robot drilling, based on active suppression of drill sliding through a model-based force control scheme. The methods are validated in a number of drilling experiments using an industrial robot.

Many contact operations in robotics require accurate positioning, which is made difficult by the presence of rapidly varying interaction forces and compliances in gear boxes and links. In order to compensate for such effects, rapid feedback from the measured tool position in several degrees of freedom is needed. This paper presents a dynamic visual tracking technique based directly on intensity measurements in the image, which can be used to obtain state estimates at a very high rate, and with very short input-output latency. Methods for analysis of the stability and sensitivity to disturbances are presented, and an improved version for better disturbance suppression of illumination variations and noise is developed. Positioning experiments using an industrial robot with camera feedback at 250 Hz are used to validate the approach

In this paper, the results from a joint industry-academia project in industrial robotic force control are presented. The extension and implementation of an external sensor system for an industrial robot system, which can be used for high-bandwidth force control, are described. Results from two industrial applications using the system are presented, a stub grinding application using a new compliant grinding end-effector integrated with the robot control system, and a deburring application with a stiff tool requiring high-bandwidth force control in six degrees of freedom. Using the system an easily reconfigurable control structure was achieved, which was able to control contact forces with a sampling bandwidth of an order of magnitude higher than for conventional robot controllers

A flexible method for six-degree-of-freedom combined vision/force control for interaction with a stiff uncalibrated environment is presented. An edge-based rigid-body tracker is used in an observer-based controller, and combined with a six-degree-of-freedom force- or impedance controller. The effect of error sources such as image space measurement noise and calibration errors are considered. Finally, the method is validated in simulations and a surface following experiment using an industrial robot.

In vision-based feedback control systems, the time to obtain sensor information is usually nonnegligible, and these systems thereby possess fundamentally different timing behavior compared to standard real-time control applications. For many image-based tracking algorithms, however, it is possible to trade-off the computational time versus the accuracy of the produced position/orientation estimates. This paper presents a method for optimizing the use of computational resources in a multicamera based positioning system. A simplified equation for the covariance of the position estimation error is calculated, which depends on the set of cameras used and the number of edge detection points in each image. An efficient algorithm for selection of a suitable subset of the available cameras is presented, which attempts to minimize the estimation covariance given a desired, prespecified maximum input-output latency of the feedback control loop. Simulations have been performed that capture the realtime properties of the vision-based tracking algorithm and the effects of the timing on the performance of the control system. The suggested strategy has been compared with heuristic algorithms, and it obtains large improvements in estimation accuracy and performance for objects both in free motion and under closed-loop position control.

In this paper a method for contour-based rigid body tracking with simultaneous camera calibration is developed. The method works for a single eye-in-hand camera with unknown hand-eye transformation, viewing a stationary object with unknown position. The method uses dual quaternions to express the relationship between the camera and end-effector screws. It is shown how using the measured motion of the robot end-effector can improve the accuracy of the estimation, even if the relative position and orientation between sensor and actuator is completely unknown. The method is evaluated in simulations on images from a real-time 3D rendering system. The system is shown to be able to track the pose of rigid objects and changes in intrinsic camera parameters, using only rough initial values for the parameters. The method is finally validated in an experiment using real images from a camera mounted on an industrial robot.

When designing flexible multi-sensor based robot systems, one important problem is how to combine the measurements from disparate sensors such as cameras and force sensors. In this paper, we present a method for combining direct force control and visual servoing in the presence of unknown planar surfaces. The control algorithm involves a force feedback control loop and a vision based reference trajectory as a feed-forward signal. The vision system is based on a constrained image-based visual servoing algorithm designed for surface following, where the location and orientation of the planar constraint surface is estimated online using position-, force- and visual data. We show how data from a simple and efficient camera calibration method can be used in combination with force and position data to improve the estimation and reference trajectories. The method is validated through experiments involving force controlled drawing on an unknown surface. The robot will grasp a pen and use it to draw lines between a number of markers drawn on a white-board, while the contact force is kept constant. Despite its simplicity, the performance of the method is satisfactory.