Vector Field Control Methods for Discretely Variable Passive Robotic Devices

Passive transmission-based robotic devices are capable of providing motion guidance while ensuring user safety and engagement. To circumvent some of the drawbacks associated with steering continuously variable transmissions based on rolling contacts, we are exploring a class of discretely variable devices, based on brakes and hydrostatic transmissions. Previously available control methods for discretely variable devices were built on velocity fields and only developed to stabilize a 1D target manifold. For n -DOF devices, methods to stabilize target manifolds of dimension 1 to n—1 are of interest. In this paper we contribute constraint field methods that stabilize n — 1 dimensional target manifolds while leaving the orthogonal subspace free to the control of the operator. We also contribute force-modulated SDOF velocity fields, which add between 1 and n— 2 virtual DOFs to the motion of devices whose physical constraints leave one DOF. Control performance is demonstrated in simulation for 3-DOF devices capable of imposing 1-D or 2-D constraints and in experiment for 2-DOF devices imposing 1-D constraints. Our experimental apparatus features digital hydraulic transmissions that are easily configured for n-dimensional space and capable of imposing constraints of any dimension, thus motivating the contributed methods

Haptic rendering of parametric surfaces using a feedback stabilized extremal distance tracking algorithm

A new extremal distance tracking algorithm is presented for convex parametric curves and surfaces undergoing rigid body motion. The geometric extremization problem is differ-entiated with respect to time to produce a dynamical system that incorporates dependence on both surface shape and rigid body motion. Extremization then takes place by in-tegrating these dynamical equations, but with a feedback controller in place to stabilize the solution. A controller design using feedback linearization is developed that si-multaneously accounts for surface shape and motion while asymptotically achieving (and maintaining) the extremal pair. Collision detection then takes place in a framework fully analogous to that used for multibody simulation. Lo-cal stability results are extended to provide global stability for body shapes composed of pieced-together convex para-metric surface patches using a switching algorithm

Toward Controllable Hydraulic Coupling of Joints in a Wearable Robot

In this paper, we develop theoretical foundations for a new class of rehabilitation robot: body powered devices that route power between a user’s joints. By harvesting power from a healthy joint to assist an impaired joint, novel bimanual and self-assist therapies are enabled. This approach complements existing robotic therapies aimed at promoting recovery of motor function after neurological injury. We employ hydraulic transmissions for routing power, or equivalently for coupling the motions of a user’s joints. Fluid power routed through flexible tubing imposes constraints within a limb or between homologous joints across the body. Variable transmissions allow constraints to be steered on the fly, and simple valve switching realizes free space and locked motion. We examine two methods for realizing variable hydraulic transmissions: using valves to switch among redundant cylinders (digital hydraulics) or using an intervening electromechanical link. For both methods, we present a rigorous mathematical framework for describing and controlling the resulting constraints. Theoretical developments are supported by experiments using a prototype fluid-power exoskeleton

Beyond synchronization: String instability in coupled harmonic oscillator systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113675/1/rnc3229.pd

Self-Powered Robots to Reduce Motor Slacking During Upper-Extremity Rehabilitation: A Proof of Concept Study

Background: Robotic rehabilitation is a highly promising approach to recover lost functions after stroke or other neurological disorders. Unfortunately, robotic rehabilitation currently suffers from motor slacking , a phenomenon in which the human motor system reduces muscle activation levels and movement excursions, ostensibly to minimize metabolic- and movement-related costs. Consequently, the patient remains passive and is not fully engaged during therapy. To overcome this limitation, we envision a new class of body-powered robots and hypothesize that motor slacking could be reduced if individuals must provide the power to move their impaired limbs via their own body (i.e., through the motion of a healthy limb). Objective: To test whether a body-powered exoskeleton (i.e. robot) could reduce motor slacking during robotic training. Methods: We developed a body-powered robot that mechanically coupled the motions of the user\u27s elbow joints. We tested this passive robot in two groups of subjects (stroke and able-bodied) during four exercise conditions in which we controlled whether the robotic device was powered by the subject or by the experimenter, and whether the subject\u27s driven arm was engaged or at rest. Motor slacking was quantified by computing the muscle activation changes of the elbow flexor and extensor muscles using surface electromyography. Results: Subjects had higher levels of muscle activation in their driven arm during self-powered conditions compared to externally-powered conditions. Most notably, subjects unintentionally activated their driven arm even when explicitly told to relax when the device was self-powered. This behavior was persistent throughout the trial and did not wane after the initiation of the trial. Conclusions: Our findings provide novel evidence indicating that motor slacking can be reduced by self-powered robots; thus demonstrating promise for rehabilitation of impaired subjects using this new class of wearable system. The results also serve as a foundation to develop more sophisticated body-powered robots (e.g., with controllable transmissions) for rehabilitation purposes

On-Line Symbolic Constraint Embedding for Simulation of Hybrid Dynamical Systems

In this paper we present a simulator designed to handle multibody systems with changing constraints, wherein the equations of motion for each of its constraint configurations are formulated in minimal ODE form with constraints embedded before they are passed to an ODE solver. The constraint-embedded equations are formulated symbolically according to a re-combination of terms of the unconstrained equations, and this symbolic process is undertaken on-line by the simulator. Constraint-embedding undertaken on-the-fly enables the simulation of systems with an ODE solver for which constraints are not known prior to simulation start or for which the enumeration of all constraint conditions would be unwieldy because of their complexity or number. Issues of drift associated with DAE solvers that usually require stabilization are sidestepped with the constraint-embedding approach. We apply nomenclature developed for hybrid dynamical systems to describe the system with changing constraints and to distinguish the roles of the forward dynamics solver, a collision detector, and an impact resolver. We have prototyped the simulator in MATLAB and demonstrate the design using three representative examples.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43263/1/11044_2005_Article_269.pd

Regenerative peripheral nerve interfaces for real-time, proportional control of a Neuroprosthetic hand

Abstract Introduction Regenerative peripheral nerve interfaces (RPNIs) are biological constructs which amplify neural signals and have shown long-term stability in rat models. Real-time control of a neuroprosthesis in rat models has not yet been demonstrated. The purpose of this study was to: a) design and validate a system for translating electromyography (EMG) signals from an RPNI in a rat model into real-time control of a neuroprosthetic hand, and; b) use the system to demonstrate RPNI proportional neuroprosthesis control. Methods Animals were randomly assigned to three experimental groups: (1) Control; (2) Denervated, and; (3) RPNI. In the RPNI group, the extensor digitorum longus (EDL) muscle was dissected free, denervated, transferred to the lateral thigh and neurotized with the residual end of the transected common peroneal nerve. Rats received tactile stimuli to the hind-limb via monofilaments, and electrodes were used to record EMG. Signals were filtered, rectified and integrated using a moving sample window. Processed EMG signals (iEMG) from RPNIs were validated against Control and Denervated group outputs. Results Voluntary reflexive rat movements produced signaling that activated the prosthesis in both the Control and RPNI groups, but produced no activation in the Denervated group. Signal-to-Noise ratio between hind-limb movement and resting iEMG was 3.55 for Controls and 3.81 for RPNIs. Both Control and RPNI groups exhibited a logarithmic iEMG increase with increased monofilament pressure, allowing graded prosthetic hand speed control (R2 = 0.758 and R2 = 0.802, respectively). Conclusion EMG signals were successfully acquired from RPNIs and translated into real-time neuroprosthetic control. Signal contamination from muscles adjacent to the RPNI was minimal. RPNI constructs provided reliable proportional prosthetic hand control.https://deepblue.lib.umich.edu/bitstream/2027.42/146521/1/12984_2018_Article_452.pd