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Biomimetic Sensing for Robotic Manipulation

Biomimetic Sensing for Robotic Manipulation. Neil Petroff, Ph. D. Candidate University of Notre Dame. Lerner Research Institute Cleveland, OH December 8, 2005. Outline. Me on Me Grasping biology as motivation for current work Robotic Manipulation Nonholonomic motion planning

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Biomimetic Sensing for Robotic Manipulation

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  1. Biomimetic Sensing for Robotic Manipulation Neil Petroff, Ph. D. Candidate University of Notre Dame Lerner Research Institute Cleveland, OH December 8, 2005

  2. Outline • Me on Me • Grasping • biology as motivation for current work • Robotic Manipulation • Nonholonomic motion planning • Motion planning for stratified systems • Open-Chain Manipulators • Forward kinematics • Inverse kinematics • Biomimetic Robot Sensors • Vision, touch • Control Perspective on Deep Brain Stimulation • The Rest of the Story

  3. Hand Orthosis Target Group: C5 - C7 SCI • 3 Grasps • Fingertip, key, cylindrical • Increase Autonomy • Mercury Orthotics • Rehabilitation technology • therapeutic • quality of life

  4. Grasping • Interaction • Creation • Task Execution Grasping Hand Orthosis Robotic Manipulation Fuzzy Logic Open-Chain Manipulators Biomimetic Robot Sensors Work to Date

  5. Grasping Robots Humans Poor at fine motion good at fine motion No feedback vision, proprioception structured adaptive precise robust rapid slow strong variable stamina need to rest Can we improve robotic manipulation by imbuing robots with useful human characteristics? Grasping Hand Orthosis Robotic Manipulation Fuzzy Logic Open-Chain Manipulators Biomimetic Robot Sensors Work to Date

  6. Biological Motivation • Haptic Recognition • Force feedback • Compliance is Useful for Manipulation • Brain Model • Fuzzy logic • Hierarchical Control Grasping Hand Orthosis Robotic Manipulation Fuzzy Logic Open-Chain Manipulators Biomimetic Robot Sensors Work to Date

  7. Biological Control Loop current configuration desired task motion planning algorithm inverse kinematics encoder counts PID Robot fuzzy supervisor trajectory adjustment sensor readings encoder counts

  8. Testbed

  9. Robotic Motion Planning • Steering Using Piecewise Constant Inputs • This is a geometric analysis • Provides a systematic approach for establishing controllability • Applicable to underactuated systems with nonholonomic constraints • Exact for nilpotent systems of the form • Driftless • Not all gi’s may exist • a system is nilpotent if all Lie brackets greater than a certain order are zero • Lie bracket motions • allows the system to move in a new direction

  10. Lie Bracket Motions Flow along g3 can be approximated by flowing along g1 and g2 Higher order brackets can be generated, e.g.

  11. Example Parallel parking a car

  12. Example Car equations l { { g1 g2 Extended System

  13. Car Simulation

  14. Why Didn’t it Work? • The Car Model is not Nilpotent • g5 points in the same direction as g3 • Motion along lower order brackets induces motion along higher order brackets • Solution • Iterate • Feedback nilpotentization • Other Drawbacks • Small Time or Small Inputs • obstacle avoidance • Open Loop • highly susceptible to modeling errors • no error correction

  15. Neither finger in contact stratum S1 M=S0 S2 g2,2 g2,1 g1,2 -g1,1 finger 2 in contact -g2,1 finger 1 in contact g1,1 S 1 2 Both fingers in contact Stratified Systems • Extends motion planning algorithm to systems with discontinuities • Intermittent contact • locomotion • manipulation

  16. Control Architecture Desired task motion planning algorithm

  17. Product-of-exponentials formula A configuration is of the form Open-Chain Manipulators Forward kinematics P s T

  18. Inverse Kinematics The inverse kinematics solution is not unique 1 1 1 1

  19. pw - pb Inverse Kinematics • PUMA geometry makes an analytical solution tractable

  20. Inverse Kinematics 14” diameter circle

  21. Control Architecture Desired task motion planning algorithm inverse kinematics current configuration encoder counts PID Robot fuzzy supervisor current counts

  22. Biomimetic Sensing

  23. Force Sensors • Feedback at Finger/Object Junction • Piezoelectric • Used in biomedical testing • Compliant • Tend to drift under static load • Flexiforce Sensor

  24. Finding an Object

  25. Control Architecture current configuration desired task motion planning algorithm inverse kinematics encoder counts PID Robot fuzzy supervisor trajectory adjustment sensor readings encoder counts

  26. Summary • So Far • Built a closed loop system to perform robotic manipulation • stratified motion planning • inverse kinematics solution • force feedback • To Do • Manipulation • Currently working on simulation • apply to robots

  27. Control Perspective on DBS(or “What the heck am I doing here?”) • Underlying manipulation technique is a geometric approach to nonlinear controls • Nonlinear control lies at the forefront of modern control methods • One of the most intriguing aspects of nonlinearity is that of chaos • Nonlinear control techniques have been used to suppress cardiac arrythmia, a chaotic process • Is neuron transmission chaotic? • at the heart of successful treatments using deep brain stimulation is the ability to control chaos • Robust and nonlinear control techniques provide an analytical foundation on which to study such systems • Soft computing techniques provide an additional approach that while not at rigorous may yield equal or better results

  28. Open Questions on DBS • By approaching DBS from a control Theory Standpoint, Can We • Control with external stimulation locally? • Filter the signals? • Characterize which signals cause which disruptions • stimulation can suppress dyskinesia • tremors tend to lessen during movement • Keep symptoms from returning with fatique? • Muscle spasticity • Completely eliminate meds?

  29. The Rest of the Story • 54,000 SCI • Additional 2,800 / yr at C5 – C6 level • Parkinson’s affects 750,000 – 1 million people in the U.S. • Other Pathologies • Hemiplegic stroke • Multiple sclerosis • Muscular dystrophy • Rehab • Funding • Competition for startup money • Who Can Pay? • Hand Mentor from KMI • $3,950 • Coverage from private insurance companies in only 2 states • Currently no medicare coverage • State of Indiana Home and Community Based Care Act • Provides funding for community and home-based care • 2002: 84 / 16 • Medicaid savings of $1,300 per client per month • Savings on the order of 3:1 when compared with institutional care

  30. My Plea • As researchers, I believe we have a responsibility to pursue noble goals • Obligation of the Engineer • “… conscious always that my skill caries with it the obligation to serve humanity …” • Hippocratic Oath • “I will remember that I do not treat a fever chart, a cancerous growth, but a sick human being, whose illness may affect the person's family and economic stability. My responsibility includes these related problems, if I am to care adequately for the sick.” • “will remember that I remain a member of society, with special obligations to all my fellow human beings, those sound of mind and body as well as the infirm.”

  31. On a Lighter Note

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