Jean-Claude Metzger

Voronoi coverage of non-convex environments with a group of networked robots

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot

BackgroundSelecting and maintaining an engaging and challenging training difficulty level in robot-assisted stroke rehabilitation remains an open challenge. Despite the ability of robotic systems to provide objective and accurate measures of function and performance, the selection and adaptation of exercise difficulty levels is typically left to the experience of the supervising therapist.MethodsWe introduce a patient-tailored and adaptive robot-assisted therapy concept to optimally challenge patients from the very first session and throughout therapy progress. The concept is evaluated within a four-week pilot study in six subacute stroke patients performing robot-assisted rehabilitation of hand function. Robotic assessments of both motor and sensory impairments of hand function conducted prior to the therapy are used to adjust exercise parameters and customize difficulty levels. During therapy progression, an automated routine adapts difficulty levels from session to session to maintain patients’ performance around a target level of 70%, to optimally balance motivation and challenge.ResultsRobotic assessments suggested large differences in patients’ sensorimotor abilities that are not captured by clinical assessments. Exercise customization based on these assessments resulted in an average initial exercise performance around 70% (62% ± 20%, mean ± std), which was maintained throughout the course of the therapy (64% ± 21%). Patients showed reduction in both motor and sensory impairments compared to baseline as measured by clinical and robotic assessments. The progress in difficulty levels correlated with improvements in a clinical impairment scale (Fugl-Meyer Assessment) (r s = 0.70), suggesting that the proposed therapy was effective at reducing sensorimotor impairment.ConclusionsInitial robotic assessments combined with progressive difficulty adaptation have the potential to automatically tailor robot-assisted rehabilitation to the individual patient. This results in optimal challenge and engagement of the patient, may facilitate sensorimotor recovery after neurological injury, and has implications for unsupervised robot-assisted therapy in the clinic and home environment.Trial registration: ClinicalTrials.gov, NCT02096445

Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function

Robot-assisted rehabilitation can complement conventional rehabilitation after stroke, by increasing the duration and intensity of therapy and providing precise and objective measurements of interaction dynamics and performance. Such information can be used to drive assist-as-needed control strategies or to complement clinical assessments by reconstructing the scores from robot data. This paper presents the ReHapticKnob, a new end-effector-based hand rehabilitation robot with unique sensing and actuation capabilities for therapy of grasping and forearm rotation tasks. A compact design with high stiffness and high-fidelity instrumentation is presented, allowing for precise assessment and dynamic interaction. The device has two degrees of freedom (DOF), allowing independent control of hand opening/closing and forearm rotation. Each degree of freedom is equipped with a brake to allow independent training of either DOF or to assess isometric force through the two six-axis force/torque sensors located beneath the exchangeable finger fixations. The design, safety features and performance evaluation of the device are discussed and preliminary results from a study on grasping performed with healthy subjects are presented.

Distributed Coverage Control on Surfaces in 3D Space

This paper addresses the problem of deploying a group of networked robots on a non-planar surface embedded in 3D space. Two distributed coverage control algorithms are presented that both provide a solution to the problem by discrete coverage of a graph. The first method computes shortest paths and runs the Lloyd algorithm on the graph to obtain a centroidal Voronoi tessellation. The second method uses the Euclidean distance measure and locally exchanges mesh cells between approximated Voronoi regions to reach an optimal robot configuration. Both methods are compared and evaluated in simulations and in experiments with five robots on a curved surface.

Physical Student–Robot Interaction With the ETHZ Haptic Paddle

Haptic paddles-low-cost one-degree-of-freedom force feedback devices-have been used with great success at several universities throughout the US to teach the basic concepts of dynamic systems and physical human-robot interaction (pHRI) to students. The ETHZ haptic paddle was developed for a new pHRI course offered in the undergraduate Mechatronics Focus track of the Mechanical Engineering curriculum at ETH Zurich, Switzerland. Twenty students engaged in this 2-h weekly lecture over the 14 weeks of the Autumn 2011 semester, complemented by a weekly 2-h laboratory session with the ETHZ haptic paddle. In pairs, students worked through three common sets of experiments before embarking on a specialization project that investigated one of several advanced topics such as impedance control with force feedback, admittance control, the effect of velocity estimation on stability, or electromyographic control. For these projects, students received additional hardware, including force sensors, electrooptical encoders or high-performance data acquisition cards. The learning objectives were developed in the context of an accompanying faculty development program at ETH Zurich; a set of interactive sequences and the oral exam were explicitly aligned to these learning objectives. The outcomes of the specialization project presentations and oral exams, and a student evaluation of the course, demonstrated that the ETHZ haptic paddle is a valuable tool that allows students to quite literally grasp abstract principles such as mechanical impedance, passivity, and human factors and helps students create a tangible link between theory and practice in the highly interdisciplinary field of pHRI.

High-fidelity rendering of virtual objects with the ReHapticKnob - novel avenues in robot-assisted rehabilitation of hand function

Rehabilitation robots can provide intensive and motivating therapy after stroke in order to further promote recovery of sensorimotor function. To provide the necessary patient-robot interaction for the assessment and training of the hand throughout the different phases of recovery, high-fidelity haptic interfaces with a wide impedance width (Z-width) are required. In this paper the Z-width and haptic interaction quality of a 2 degree-of-freedom (DOF) end-effector based hand rehabilitation robot called the ReHapticKnob are evaluated and strategies to improve these parameters are investigated. An impedance-based controller with force feedback was implemented to modulate the apparent impedance of the robots end-effector. Additionally, a discrete-time adaptive velocity estimator was used to increase the Z-width of the device. The resulting impedance is evaluated and compared to a commercial haptic device (Phantom Premium 1.5) and the achieved Z-width is analyzed in frequency space and on a K-B-plot. With the proposed control strategy the ReHapticKnob shows similar transparent behavior as a Phantom Premium 1.5 but can render much higher impedances, resulting in a unique high-fidelity patient-robot interaction capable of adapting to different impairments and presenting various haptic stimuli.

Neurocognitive Robot-Assisted Therapy of Hand Function

Neurocognitive therapy, according to the Perfetti method, proposes exercises that challenge motor, sensory as well as cognitive functions of neurologically impaired patients. At the level of the hand, neurocognitive exercises typically involve haptic exploration and interaction with objects of various shapes and mechanical properties. Haptic devices are thus an ideal support to provide neurocognitive exercises under well-controlled and reproducible conditions, and to objectively assess patient performance. Here we present three neurocognitive robot-assisted exercises which were implemented on the ReHapticKnob, a high-fidelity two-degrees-of-freedom hand rehabilitation robot. The exercises were evaluated for feasibility and acceptance in a pilot study on five patients suffering from different neurological disorders. Results showed that all patients were able to take part in the neurocognitive robot-assisted therapy, and that the proposed therapy was well accepted by patients, as assessed through subjective questionnaires. Force/torque and position measurements provided insights on the motor strategy employed by the patients during the exploration of virtual object properties, and served as objective assessment of task performance. The fusion of the neurocognitive therapy concept with robot-assisted rehabilitation enriches therapeutic approaches through the focus on haptics, and could provide novel insights on sensorimotor impairment and recovery.

Extensions to haptic augmented reality: Modulating friction and weight

Haptic augmented reality merges real and virtual feedback, allowing a user to touch the real environment augmented with virtual haptic stimuli. A key functionality of this technology is altering the haptic properties of real objects by means of virtual haptic feedback. Previously, we have developed a haptic augmentation system in which object stiffness was modulated. In this paper, we extend our framework to cover further haptic properties: friction and weight. Simple but effective algorithms for estimating and altering these properties have been developed. The first approach allows us to change the inherent friction between a tool tip and a surface to a desired one identified in an offline process. The second technique enables a user to perceive an altered weight when lifting an object at two interaction points. The performance of the proposed algorithms has been evaluated on real objects. The errors remain within reasonable bounds and compare well to the relevant perceptual Weber fractions. Limitations of our technique are identified, and possible extensions are proposed.

Performance comparison of interaction control strategies on a hand rehabilitation robot

Numerous upper-limb rehabilitation robots have been developed to complement physical therapy following stroke. Most systems have focused on providing passive or assisted movement therapy, largely neglecting the haptic aspects of interaction with the environment. We argue that, especially for the training of object manipulation, rehabilitation robots should be able to stably render a broad dynamic range of impedances. Furthermore, the apparent dynamics of a rehabilitation robot might alter the motion of a patient or impact assessments performed with the device, and should therefore be characterized in detail. We implemented and compared different interaction controllers on a hand rehabilitation robot, the ReHapticKnob, and characterized the dynamic performance using conventional performance metrics as well as with a human in the loop. We further propose transparency planes, an extension to the regression of interaction data proposed in the literature, as a simple performance metric allowing a visual appreciation and comparison of the apparent dynamics of a device in transparency mode. Impedance control with force feedback outperformed the other control schemes and achieved a dynamic impedance range of 82 dB at 0.5 Hz and 35 dB at 10 Hz along the linear degree of freedom of the ReHapticKnob. The implications of these results are discussed in the context of robot-assisted assessment and therapy.

What’s Your Next Move? Detecting Movement Intention for Stroke Rehabilitation

BCIs have recently been identified as a method to promote restorative neuroplastic changes in patients with severe motor impairment, such as after a stroke. In this chapter, we describe a novel therapeutic strategy for hand rehabilitation making use of this method. The approach consists of recording brain activity in cortical motor areas by means of near-infrared spectroscopy, and complementing the cortical signals with physiological data acquired simultaneously. By combining these signals, we aim at detecting the intention to move using a multi-modal classification algorithm. The classifier output then triggers assistance from a robotic device, in order to execute the movement and provide sensory stimulation at the level of the hand as response to the detected motor intention. Furthermore, the cortical data can be used to control audiovisual feedback, which provides a context and a motivating training environment. It is expected that closing the sensorimotor loop with such a brain-body-robot interface will promote neuroplasticity in sensorimotor networks and support the recovery process.