Aitziber Mancisidor

Virtual Sensors for Advanced Controllers in Rehabilitation Robotics

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.

Kinematic and Dynamic Modeling of a Multifunctional Rehabilitation Robot UHP

The design of a suitable controller that handles robot-human interaction is one of the critical tasks in rehabilitation robotics. For this purpose, an accurate model of the robot is required. The Universal Haptic Pantograph (UHP) is a novel upper limb rehabilitation robot that can be configured to perform arm or wrist exercises. This work is focused on the latter, solving the kinematic model by the use of the closure loop equations, while Lagrangian formulation is used to estimate the interaction force. In order to prove the effectiveness of the model, several experimental tests are carried out. Results demonstrate that the mean motion error is less than 1 mm, and the estimated force error less than \(10\%\).

A stable model-based control scheme for parallel robots using additional sensors

The use of parallel robots has been demonstrated to be an interesting alternative when high accuracy and/or high speed is required. However, in order to achieve these goals, model based controllers are required. This work presents a new model based control approach, the stable Extended CTC, that uses extra data from additional sensors introduced in the passive joints of parallel robot in the controller. The proposed controller guarantees asymptotic stability, which is an important contribution over the previously presented approaches. The use of redundant information increases controller robustness and performance, allowing to reduce tracking error with respect to traditional CTC approaches. The effectiveness of the proposed control law is demonstrated by implementing it in a Delta robot which has been modeled in ADAMS multibody software.

Enhanced force control using force estimation and nonlinearity compensation for the Universal Haptic Pantograph

The design of a stable and robust force controller is one of the most important and difficult tasks in rehabilitation robotics. In previous works, the Universal Haptic Pantograph (UHP) was presented as an alternative to conventional arm rehabilitation after a stroke. This robot is composed by a Series Elastic Actuator (SEA) and a Pantograph. In this work an enhanced force control for the UHP is presented. The proposed controller uses the robot model to estimate the contact force without direct measurement and to compensate nonlinearities in the actuators. In order to prove the effectiveness of the approach, several tests are carried out in simulation and experimentally. Results reveal that mean of tracking errors between desired and actual force is smaller than 0.1 N, which is significantly improved compare to that (around 2.5 N) shown in previous results of UHP, indicating that the proposed force control is likely to enhance haptic performance of the UHP.

Measurement of Complementary Trunk Movement in Robot-Mediated Upper Limb Rehabilitation

In this paper, we present a method and software that measure the trunk movement of the patients during the robot-mediated rehabilitation for upper limbs. Usually, people with reduced mobility such as stroke patients unintentionally use their trunk to satisfy goals of trainings (e.g. reaching specific points), which reduces outcomes of the rehabilitation therapies. Hence it’s important to monitor trunk movement during trainings. The method utilizes two IMUs (Inertial Measurement Unit) placed in back and affected upper arm. The developed software displays the trunk movement in 3D avatar and plots while providing the estimated percentage of trunk involvement in therapy. To evaluate the method and software, they have been implemented in the UHP upper limb rehabilitation robot and tested through reaching exercises with a group of 25 healthy people. The results show that the method works properly in measuring trunk motion during training.

Virtual Sensor for Kinematic Estimation of Flexible Links in Parallel Robots

The control of flexible link parallel manipulators is still an open area of research, endpoint trajectory tracking being one of the main challenges in this type of robot. The flexibility and deformations of the limbs make the estimation of the Tool Centre Point (TCP) position a challenging one. Authors have proposed different approaches to estimate this deformation and deduce the location of the TCP. However, most of these approaches require expensive measurement systems or the use of high computational cost integration methods. This work presents a novel approach based on a virtual sensor which can not only precisely estimate the deformation of the flexible links in control applications (less than 2% error), but also its derivatives (less than 6% error in velocity and 13% error in acceleration) according to simulation results. The validity of the proposed Virtual Sensor is tested in a Delta Robot, where the position of the TCP is estimated based on the Virtual Sensor measurements with less than a 0.03% of error in comparison with the flexible approach developed in ADAMS Multibody Software.

Kinematic Estimator for Flexible Links in Parallel Robots

Control of flexible link parallel manipulators is still an open area of research. The flexibility and deformations of the limbs make the estimation of the Tool Center Point (TCP) position a non-trivial area, being one of the main challenges on this type of robots. In the literature different approaches to estimate this deformation and determine the location of the TCP have been proposed. However, most of these approaches require the use of high computational cost integration methods or expensive measurement systems. This work presents a novel approach which can not only estimate precisely the deformation of the flexible links (less than 3% error), but also its derivatives (less than 4% error). The validity of the developed estimator is tested in a Delta Robot, resulting in less than 0.025% error in the estimation of the TCP position in comparison with the results obtained with ADAMS Multibody software.

A Comprehensive Training Mode for Robot-Mediated Upper Limb Rehabilitation

Defining rehabilitation robots behavior during training exercises is necessary for their optimum performance. In this work, a comprehensive training mode for an upper limb rehabilitation robot, the UHP (Universal Haptic Pantograph) is presented. The proposed mode, which is divided in three phases, focuses on upper limb extension allowing the task to be adapted to the recovery state of the patient and ensuring exercise completion. Experimental validation of the training mode is carried out with the upper limb rehabilitation robot UHP.