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UI ECE 591 - Optimizing Compliant, Model-Based Robotic Assistance

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Optimizing Compliant, Model-Based Robotic Assistance to Promote NeurorehabilitationMotivation for Robotic Movement TrainingCan Robotic Devices Help?1st Generation Devices for Movement Training after Stroke2nd Generation Devices for Movement Training after StrokePneu-WREX: Development HistoryWhy Choose Pneumatics?Design FeaturesSafety FeaturesSensingData Acquisition and ControlState EstimationState Estimation: AdvantagesServovalve Characterization for Improved Force ControlHypothesis: An “Optimal” Movement Training Controller Should:Selecting a Controller for “Optimal” Movement TrainingAdaptive, Assist-As-Needed ControllerNeuromuscular Weakness ModelPneu-WREXTesting the Adaptive ControllerController Helps Complete MovementsController Learns Assistance Force for Different Arm WeightsWith “Forgetting”, Controller Reduces Force when Errors are SmallWithout “Forgetting”, Subject Allows Robot to “Take-Over”With “Forgetting”, Subject Contribution IncreasesWith “Forgetting”, Assistance is Proportional to ImpairmentTherapy GamesTherapy with Robotic AssistancePlaying without Robotic AssistanceSlide 30Robotic Assessments: Game TimeRobotic Assessments: Reaching SpeedsSummaryAcknowledgementsAppendixAppendix: System DynamicsSlide 37Appendix: Force DynamicsAppendix: Lyapunov Function CandidateForce Dynamics and Chamber Force SelectionForce ControllerSingle Cylinder Force Tracking to 40 HzPosition Tracking to 2 HzPosition Testing ResultsController Modification: Assist-As-NeededOptimizing Compliant, Model-Based Robotic Assistance to Promote NeurorehabilitationEric Wolbrecht, PhDAssistant Professor, Department of Mechanical EngineeringUniversity of IdahoThis majority of this work was completed at the Department of Mechanical and Aerospace EngineeringUniversity of California, IrvineSupported by NIH N01-HD-3-3352and NCRR M01RR00827Motivation for Robotic Movement Training•A stroke is suffered by over 700,000 people in the U.S. each year, making it a leading cause of severe, long term disability.•80 percent of stroke victims experience upper extremity movement impairment.•The estimated direct and indirect cost of stroke for 2007 is $62.7 billion.•Stoke rate to increase as population ages.Can Robotic Devices Help?•Evidence suggests that intensive, repetitive sensory motor training can improve functional recovery.•Traditional hands-on therapy is expensive and labor intensive, and therefore patients receive limited amounts of it.•One possible solution to this problem is to develop robotic devices to automate functional motor training.Robotic movement training with Pneu-WREX•It was previously believed that movement recovery was possible only for acute patients (< 6 months post-stroke). Research has shown that recovery is possible for people with chronic stoke as well (>6 months post-stroke).1st Generation Devices forMovement Training after StrokeARM Guide (UCI)Proportional derivative control,Active ConstrainedMIT-ManusImpedance controlImpedance channeltoward targetMIME (Stanford)Proportionalderivative control,Active constrainedand bilateral modes2nd Generation Devices forMovement Training after StrokeARMin (Zurich)PD control & gravitycompensationVertical Modulefor MIT-ManusImpedance control &gravity compensationRUPERT (Arizona State)Open loop controlPneu-WREX: Development History•An offspring of WREX (Wilmington Robotic Exoskeleton), a passive gravity balancing orthosis (Rahman et al, 2000.)•WREX was modified to create T-WREX (Training-WREX), a sensorized, passive gravity balancing orthosis (Sanchez et al, 2004.) •Pneu-WREX (Pneumatic-WREX) was created by adding pneumatic actuators to T-WREX (Sanchez, Wolbrecht et al, 2005.) •Current research focuses on promoting recovery through advanced control (Wolbrecht et al, 2006, 2007.)WREXT-WREXPneu-WREXWhy Choose Pneumatics?•Advantage of Pneumatics–Large power to weight ratio–Clean, and inexpensive.–Force controllable.–Backdrivable and compliant.–Inherent compliance increases safety.•Disadvantages of Pneumatics–Non-linear friction–Require advanced control–Not all facilities have compressed air and in-room compressors can be expensive and noisy.Design FeaturesSpring Counterbalance MechanismServovalves (2 per cylinder)•Two servovalves per cylinder, keeping air consumption and friction low.•Uses a spring to counterbalances the weight of the orthosis, expanding the vertical force range.•4 degrees-of-freedom, lightweight, compliant.•Strong (can apply > 50 N of force at hand).•Grip handle with grip sensor.Safety Features•Spring counterbalance provides a safe transition during an e-stop.•Normally exhausting main valve controls system air supply and is vented during emergency-stop or a detected failure.•Pneumatics are inherently compliant and maximum force is limited.•Workspace of device is less than the workspace of the arm.•Numerous software checks, including a check of redundant position sensors.Pneu-WREXSensing8 pressure sensors,HoneywellASCX100AM4 cylinders with LTR potentiometers, Bimba PFC4 angular potentiometers, Midori CP-2fb2, 2-axes MEMS accelerometers, Analog Devices ADXL320EBData Acquisition and Control•Controller developed in The Mathworks Simulink® and executed using the xPC Target real-time operating system. •Data input and output using four Measurement Computing PCI cards–(3) PCIM-DAS1602/16, 8 Differential A/D, 2 D/A, 16 bit–(1) PCI-DDA08/16, 8 channel D/A, 16 bit•1 kHz sampling rate•Target Execution Time (TET) ≈ 650 μs A/D, D/A PCI Card, Measurement ComputingPCIM-DAS1602/16 D/A PCI Card, Measurement ComputingPCIM-DDA08/16State Estimation•State Estimation using MEMS accelerometers in a Kalman Filter•Estimated velocity and position signals have reduced noise and phase lag compared to a conventionallow-pass filter.State Estimation: Advantages•Signals have less noise and less phase lag•Improved stability•Quieter operation•Reduced air consumptionServovalve Characterizationfor Improved Force Control•Experimentally determined flow map equation to linearize airflow through the servovalves.•Separate maps for both inflow and outflow.( ),d cu f m p=&control signal (volts)=desired flow rate (SLPM)=dm&ucpchamber pressure (kPA)=Hypothesis: An “Optimal” Movement Training Controller Should:1. Help Complete Movements. Stimulate afferent signals from the arm by assisting patients in making spatial movements with small errors, overcoming


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