LegWkshp 5 99 final

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Published on January 7, 2008

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Biomimetic Robots for Robust Operation in Unstructured Environments:  Biomimetic Robots for Robust Operation in Unstructured Environments R. Howe Harvard University M. Cutkosky and T. Kenny Stanford University R. Full and H. Kazerooni U.C. Berkeley R. Shadmehr Johns Hopkins University http://cdr.stanford.edu/touch/biomimetics ONR/DARPA MEETING ON LEGGED ROBOTS, COOPERATIVE BEHAVIOR, AND NAVIGATION COSTAL SYSTMS STATION, PANAMA CITY, MAY 4-5, 1999 BioMimetic Robotics MURI Berkeley-Harvard Hopkins-Stanford Main ideas::  Main ideas: Study insects to understand role of passive impedance (structure and control), study humans to understand adaptation and learning (Full, Howe,Shadmehr) Use novel layered prototyping methods to create compliant biomimetic structures with embedded sensors and actuators (Cutkosky, Full, Kenny) Develop biomimetic actuation and control schemes that exploit “preflexes” and reflexes for robust locomotion and manipulation (Full, Cutkosky, Howe, Kazerooni, Shadmehr) BioMimetic Robotics MURI Berkeley-Harvard Hopkins-Stanford Status - Locomotion:  Status - Locomotion First year of project Preliminary experiments to determine insect leg properties Fabricated first prototypes of embedded sensors and actuators Locomotion focus: rough terrain traversal, inspired by cockroach running over blocks surface ~3x “shoulder” height BioMimetic Robotics MURI Berkeley-Harvard Hopkins-Stanford MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Motor Control & Learning Johns Hopkins Sensors / MEMS Stanford Manipulation Harvard MURI Locomotion UC Berkeley Bob Full Robots & Legs UC Berkeley Neuro-Mechanical Model:  Neuro-Mechanical Model Mechanical Higher Centers Environment Aero-, hydro-, terra-dynamic Feedforward Controller (CPG) Adaptive Controller Sensors Closed-loop Open-loop System (Actuators, limbs) Feedback Controller Sensors Behavior Neuro-Mechanical Model:  Neuro-Mechanical Model Mechanical Feedforward Controller (CPG) Closed-loop System (Muscles, limbs) Behavior Sensors Reflexive Neural Feedback Contribution to Control:  Neural System Contribution to Control Feedforward Intrinsic musculo- skeletal properties Preflex Reflex Motor program acting through moment arms Passive Dynamic Self-stabilization Active Stabilization Neural feedback loops Mechanical System Predictive Rapid acting Slow acting Slide8:  Force Perturbation Animal Strikes Obstacle Smaller Reaction Force Joint Angles Altered Less Stable Decreased Speed Perturbation Response No Preflex Preflex Working Hypotheses Larger Reaction Force Joint Angles Similar More Stable Maintain Speed Discoveries:  Discoveries Preflex Present No Active Reflex Required Stiffness Varies During Cycle Slide10:  Perturbation Experiments Muscle is Stiffest at Midstance Leg Stiffness:  Leg Stiffness 1st Measures of Leg Stiffness, Damping Servo Motor Roach leg Length and Force recording Impact on Deliverables:  Impact on Deliverables 1. Flexible Robot Leg Could Reject Perturbations 2. Simplify Control (feedforward) 3. Suggest Design of Artificial Muscles Micromachined Force Sensor for Adhesion Force Measurement of Single Gecko Setae:  Micromachined Force Sensor for Adhesion Force Measurement of Single Gecko Setae Yiching Liang and Tom Kenny Stanford University ~106 setae per animal, average 4.7 m diameter Wall climbing mechanisms: Suction, Capillary (wet) adhesion, Micro-interlocking, Electrostatic attraction - NOT; van der Waals forces? MEMS Instrumentation for biomechanics studies (Kenny/Full) Slide14:  Special 45 ion implantation to embed piezoresistors on surfaces and side walls. 2-Axis Micromachined Force Sensor Attachment point Gecko measurements now underway... MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Motor Control & Learning Johns Hopkins Reza Shadmehr Sensors / MEMS Stanford Manipulation Harvard MURI Locomotion UC Berkeley Robots & Legs UC Berkeley Neuro-Mechanical Model:  Neuro-Mechanical Model Mechanical Higher Centers Environment aero- , hydro, terra-dynamic Feedforward Controller (CPG) Adaptive Controller Sensors Closed-loop Open-loop System (Actuators, limbs) Feedback Controller Sensors Behavior Relating Limb Impedance and Learning:  General Goal: Understand human arm impedance strategies when learning tasks in unstructured environments Challenges: The biomechanics of the human arm are dominated by multiple time delays in feedback. How do time delays affect measures of arm impedance? Humans learn internal models to learn control. How does a change in the internal model affect measures of arm impedance? Relating Limb Impedance and Learning Results:  In general, time delays in feedback reduce apparent viscosity and add apparent mass to a system. Example: Results Human Arm Motor Control Model:  A model of the human arm’s time-delayed control processes were used to derive bounds on the impedance changes that should occur as a function of learning. Human Arm Motor Control Model Implications for Robot Control:  Relates delays to variation in limb impedance - convenient means of analyzing mechanical interactions Method for trading off “costs” of higher-level processing delay vs. passive impedance Implications for Robot Control MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Motor Control & Learning Johns Hopkins Sensors / MEMS Stanford Manipulation Harvard Robert Howe MURI Locomotion UC Berkeley Robots & Legs UC Berkeley Impedance in Manipulation:  Impedance in Manipulation Example: Grasping in an unstructured environment Before contact: No interaction force => Low arm stiffness k Collision with object produces small disturbance force Muscle Impedance f = k x Variable Impedance Manipulation Testbed:  Variable Impedance Manipulation Testbed Whole-Arm Manipulator (Barrett Technologies) Low moving mass Negligible friction Back driveable => Low impedance robot Goal: Minimum Impedance Grasping and Maniplation:  Goal: Minimum Impedance Grasping and Maniplation Combine biologically-inspired elements: low-impedance manipulator feedforward dynamic models (limit feedback gains to reduce impedance) simple contact sensing “Intrinsic” tactile sensing (contact location from force-torque measurements)` => Ability to probe and grasp objects with minimum forces in unstructured environments MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Motor Control & Learning Johns Hopkins Sensors / MEMS Stanford Manipulation Harvard MURI Locomotion UC Berkeley Robots & Legs UC Berkeley Hami Kazerooni Objectives:  Objectives Create a robust, simple, and fast legged platform, able to traverse rough block surface Use off-the-shelf fabrication technology Explore role of open-loop impedance and mechanical design Serve as early testbed for control concepts Initial Focus: Leg Mechanism:  Initial Focus: Leg Mechanism Biological Observations Control results from the properties of the parts and their morphological arrangement. Musculoskeletal units and legs do much of the computations on their own by using segment mass, length, inertia, elasticity, and damping as “primitives”. Engineering Equivalence System performance is function of the physical system; no feedback control has been used to alter the dynamics of the system. Full has shown that a substantial portion of locomotor control is simple and resides in the mechanical design of the system Slide28:  Biological Observations Position control using reflexes is improbable if not impossible During climbing, turning, and maneuvering over irregular terrain, animals use virtually the same gait as in horizontal locomotion - an alternating tripod. The animals appear to be playing the same feedforward program for running. Engineering Equivalence No need for sensors for position speed, or force control A one degree of freedom system only. No need to design elaborate multi-variable robotic legs. 1-DOF Linkage Design Example:  1-DOF Linkage Design Example a b c d f g MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Mark Cutkosky Motor Control & Learning Johns Hopkins Sensors / MEMS Stanford Tom Kenny Manipulation Harvard MURI Locomotion UC Berkeley Robots & Legs UC Berkeley Application: Small robots with embedded sensors and actuators:  Application: Small robots with embedded sensors and actuators Building small robot legs with pre-fabricated components is difficult… Is there a better way? Shape Deposition Manufacturing (CMU/SU):  Deposit (part) Shape Embed Deposit (support) Shape Shape Deposition Manufacturing (CMU/SU) Embedded Components + Soft materials => Improved robustness Simplified construction Robot leg example (http://cdr.stanford.edu/biomimetics):  Designer composes the design from library of primitives, including embedded components Steel leaf spring Piston Outlet for valve Valve Primitive Circuit Primitive Inlet port primitive Part Primitive Robot leg example (http://cdr.stanford.edu/biomimetics) Slide36:  The output of the software is a sequence of 3D shapes and toolpaths. Robot Leg: compacts Support Part Embedded components Robot Leg: embedded parts:  A snapshot just after valves and pistons were inserted. Steel leaf-spring Piston Sensor and circuit Valves Robot Leg: embedded parts Slide38:  Pressure Control in Small Pneumatic Systems t t Pressure Control Impossible PWM Control Equal t line Performance Small Pneumatic Systems Usual regime of Operation Solenoid Valves volume SDM allows fabrication of small integrated mechanisms Control of small pneumatic systems with off-the-shelf components (solenoid valves) is in a challenging regime Miniature analog servo-valves needed for smooth performance are not available Different Sensors and Actuators have different considerations for embedding, generally these include::  Different Sensors and Actuators have different considerations for embedding, generally these include: Coupling and Adhesion Fixturing, Positioning, Placement Protection and Encapsulation Multiplexing, Connectivity, Interconnect Integrity and Strain Relief Thermal energy generation and cooling SDM Considerations for Embedded Sensors/Actuators Slide40:  Sensor circuit boards - interconnect pins protected in wax before embedding Circuit boards embedded with pressure sensor--sensor ports protected with wax Slide41:  Embedded sensor and circuitry with sacrificial wax removed Assembled into pneumatic system Slide42:  Finished parts ready for testing Robot Leg: completed MURI Interactions: Areas and Leadership:  MURI Interactions: Areas and Leadership Muscles and Rapid Prototyping Stanford Motor Control & Learning Johns Hopkins Sensors / MEMS Stanford Manipulation Harvard MURI Locomotion UC Berkeley Robots & Legs UC Berkeley

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