BioMimetic Robotics MURI Berkeley-Harvard Hopkins-Stanford 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:

• 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

• 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

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Rapid Prototyping Stanford MURI Manipulation Harvard Sensors / MEMS Stanford Muscles and Locomotion UC Berkeley Bob Full Robots & Legs UC Berkeley

Neuro-Mechanical Model

Higher Centers Sensors Open-loop Feedforward Controller (CPG) Aero-, hydro-, terra-dynamic Feedback Controller

Environment Mechanical System

(Actuators, limbs) Closed-loop Behavior Adaptive Controller Sensors

Neuro-Mechanical Model

Mechanical Feedback Feedforward Controller (CPG) Mechanical System

(Muscles, limbs)

Behavior Sensors

Closed-loop

Reflexive Neural Feedback

Contribution to Control

Mechanical System

Feedforward Preflex

Motor program acting through moment arms Intrinsic musculo skeletal properties

Neural System

Reflex

Neural feedback loops

Predictive Rapid acting Slow acting

Passive Dynamic Self-stabilization Active Stabilization

Perturbation Response

Working Hypotheses Force Perturbation

Animal Strikes Obstacle

Smaller Reaction Force Joint Angles Altered Less Stable Decreased Speed Larger Reaction Force Joint Angles Similar More Stable Maintain Speed

Discoveries

•

Preflex Present

•

No Active Reflex Required

•

Stiffness Varies During Cycle

Perturbation Experiments

Muscle is Stiffest at Midstance

105 Active 100 95 0 50 Locomotion cycle (%) 100

1

st

Measures of Leg Stiffness, Damping

Leg Stiffness

Servo Motor Roach leg Length and Force recording

Impact on Deliverables

1.

Flexible Robot Leg Could Reject Perturbations 2.

Simplify Control (feedforward) 3.

Suggest Design of Artificial Muscles

MEMS Instrumentation for biomechanics studies (Kenny/Full)

Micromachined Force Sensor for Adhesion Force Measurement of Single Gecko Setae Yiching Liang and Tom Kenny Stanford University ~10 6 setae per animal, average 4.7  m diameter Wall climbing mechanisms: Suction, Capillary (wet) adhesion, Micro-interlocking, Electrostatic attraction - NOT; van der Waals forces?

2-Axis Micromachined Force Sensor Special 45  ion implantation to embed piezoresistors on surfaces and side walls.

lateral sensor vertical sensor Attachment point Lightly doped (piezoresistive) Heavily doped (highly conductive)

Gecko measurements now underway...

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Reza Shadmehr Rapid Prototyping Stanford MURI Manipulation Harvard Sensors / MEMS Stanford Muscles and Locomotion UC Berkeley Robots & Legs UC Berkeley

Neuro-Mechanical Model

Higher Centers Sensors Open-loop Feedforward Controller (CPG) aero- , hydro, terra-dynamic Feedback Controller

Environment Mechanical System

(Actuators, limbs) Closed-loop Behavior Adaptive Controller Sensors

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?

Results

In general, time delays in feedback reduce apparent viscosity and add apparent mass to a system.

Example:

m



x

 

b x

 

kx

(

t

  )  0 Using Taylor series expansion on the delay :

x

(

t

  ) 

x

(

t

) 

dx dt

(   )  1 / 2

d

2

x dt

2 (   ) 2   The resulting system has apparent (

m

 1 / 2

k

 ) 

x

  (

b



k

 )

x

 

kx

 0 impedance of :

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.

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

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Rapid Prototyping Stanford MURI Manipulation Harvard Robert Howe Sensors / MEMS Stanford Muscles and Locomotion UC Berkeley Robots & Legs UC Berkeley

Impedance in Manipulation

Muscle Impedance FORCE Example: Grasping in an unstructured environment • Before contact: No interaction force => Low arm stiffness

k

• Collision with object produces small disturbance force

f = k x

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 “Intrinsic” tactile sensing (contact location from force torque measurements)` Combine biologically-inspired elements: • low-impedance manipulator • feedforward dynamic models (limit feedback gains to reduce impedance) • simple contact sensing => Ability to probe and grasp objects with minimum forces in unstructured environments

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Rapid Prototyping Stanford MURI Manipulation Harvard Sensors / MEMS Stanford Muscles and Locomotion UC Berkeley Robots & Legs UC Berkeley Hami Kazerooni

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

Full has shown that a substantial portion of locomotor control is simple and resides in the mechanical design of the system

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.

Biological Observations • Position control using reflexes is improbable if not impossible Engineering Equivalence • No need for sensors for position speed, or force control • 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.

• A one degree of freedom system only. No need to design elaborate multi variable robotic legs.

1-DOF Linkage Design Example g a b f d c

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Rapid Prototyping Stanford Mark Cutkosky Muscles and Locomotion UC Berkeley MURI Manipulation Harvard Robots & Legs UC Berkeley Sensors / MEMS Stanford Tom Kenny

Application: Small robots with embedded sensors and actuators Shaft coupling Shaft Motor Leg links Building small robot legs with pre-fabricated components is difficult…

Is there a better way?

Shape Shape Deposition Manufacturing (CMU/SU) Deposit (part) Embedded Component Part Support Shape Deposit (support) Embed Embedded Components + Soft materials => •Improved robustness •Simplified construction

Robot leg example

( http://cdr.stanford.edu/biomimetics ) Part Primitive Outlet for valve Valve Primitive Circuit Primitive Inlet port primitive Steel leaf spring Piston

Designer composes the design from library of primitives, including embedded components

Robot Leg: compacts

The output of the software is a sequence of 3D shapes and toolpaths. Embedded components Part Support

Robot Leg: embedded parts Steel leaf-spring Piston Sensor and circuit Valves

A snapshot just after valves and pistons were inserted.

Pressure Control in Small Pneumatic Systems • 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 Air Volume Atmospheric Pressure Piston t Solenoid Valves Pressure Control Impossible Exhaust valve Pressure Sensor Inlet valve Supply Pressure PWM Control Small Pneumatic Systems Usual regime of Operation t volume

SDM Considerations for Embedded Sensors/Actuators 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

Sensor circuit boards interconnect pins protected in wax before embedding Circuit boards embedded with pressure sensor--sensor ports protected with wax

Embedded sensor and circuitry with sacrificial wax removed Assembled into pneumatic system

Robot Leg: completed

Finished parts ready for testing

MURI Interactions: Areas and Leadership

Motor Control & Learning Johns Hopkins Rapid Prototyping Stanford MURI Manipulation Harvard Sensors / MEMS Stanford Muscles and Locomotion UC Berkeley Robots & Legs UC Berkeley