Biomimetic Robots for Robust Operation in Unstructured Environments

University of California at Berkeley

H. Kazerooni

The objective is to create robust and small walking machines.

(Insects are good examples because they are small and robust.)

• Observations from biological systems

• Engineering specifications that may provide some insights as how to make machines so their behavior is similar to our observations of biological systems.

• Easy walk

• Requires little processing

• Fixed gait

• Utilizes pendulum like natural frequency to minimize energy

• One gets tired fast

• Lots of processing

• Terrain requires random gait

• Random gait will not allow any form of natural frequency -- thus walking is very inefficient

Single D.O.F (Mechanism) Multiple D.O.F (Complex Machine)

WALKING ROUGH TERRAINSMOOTH TERRAIN

Lg

n

Walking Speed is independent of load -- i.e. students walk at the same speed regardless of their backpack load

Age vs. Walking Frequency

100110120130140150160170180

0 10 20 30 40 50 60

years

step

s/m

inL

gn

You Stop Growing (L = Constant)

Single D.O.F (Mechanism) Multiple D.O.F (Complex Machine)

Running ROUGH TERRAINSMOOTH TERRAIN

MKn

Full has shown that a substantial portion of locomotor control can reside in the mechanical

design of the system and be simple:

Biological Observations

• Control results from the properties of the parts and their morphological arrangement. Musculoskeletal units, leg segments and legs do much of the computations on their own by using segment mass, length, inertia, elasticity, and damping as “primitives”.

Engineering Equivalence

• The system performance is function of the physical system; no feedback control has been used to alter the dynamics of the system.

Biological Observations

• 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.

• There is no precise foot placement, no follow the leader gait, and a leg does not have time to react to tactile sensory feedback within a step.

Engineering Equivalence

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

• Open loop within the system workspace

Biological Observations

• Position control using reflexes is improbable if not impossible

• The control algorithms are embedded in the form of the animal itself.

• The mechanical system - the morphology - can determine the extent of self-stabilization. In other words, there is no explicit feedback control of global variables such as hopping height, posture, or speed. The only control is local, at the joints.

Engineering Equivalence

• No need for sensors for position speed, or force control

• The dynamics of the system is due to the dynamics of the hardware as designed by the designer with no alteration by feedback control.

• If there is no explicit feedback control of global variables such as hopping height, posture, or speed, therefore the control space (e.g. trajectory) must be limited.

The “Classical Robotics” technology may not be effective in

design of small and robust walking machines

Design Specifications

• High Speed Mobility

• Small Size & Light Weight, (however constrained by fabrication technology, 3x4 inch body)

• No sensing, No feedback controls

• Cockroach foot path

• Compliance and Stability

• Simple Design (4 legs at this time)

• On-board power (Dictates the entire design

• Expected Speed (about 3”/sec)

Human foot Trajectory

Cockroach Foot Trajectory(Cutesy of Bob Full Lab)

LEFT LEG PATH

-10

-8

-6

-4

-2

0-30 -20 -10 0 10 20 30

Design of Mechanism to Mimic Cockroach Leg Trajectory

Path Requirements

• Slow gait on bottom, fast gait on top.• Flat path at ground contact.• Taller gait for high clearance.• Longer gait for efficient walking.• Robust geometry in the presence of

fabrication inaccuracies.

Verification of Trajectories

Experimental Machine at UC-Berkeley

size: 3.5"x3"speed: 3 inch/sec

Low-LevelControl

Fabrication

High-LevelControl

MURI

What passive properties are found in Nature?

What properties in mechanical design?

How should properties be varied for changing tasks, conditions ?Matching ideal impedance for unstructured dynamic tasks (Harvard)

Guiding questionsGuiding questions

Preflexes: Muscle and Exoskeleton Impedance Measurements (Berkeley Bio.)

Biological implications for RoboticsBasic Compliant Mechanisms for Locomotion (Stanford)Variable compliance joints (Harvard, Stanford)Fast runner with biomimetic trajectory (Berkeley ME)

BioMimetic Robotics

MURIBerkeley-HarvardHopkins-Stanford

MURI

Low-LevelControl

Minimum Impedance ControlMinimizing Interaction Forces in

Exploration and Manipulation

Jaydev P. Desai and Robert D. Howe

Harvard University

Intrinsic Finger Stiffness vs. Force4 subjects

(Hajian and Howe 1997)

0 5 10 15 200

200

400

600

800

1000

1200

stif

fnes

s (N

/m)

finger tip force (N)

Key robot capability for unstructured environments

MURI

Low-LevelControl

Impedance in Manipulation

Example: Grasping in an unstructured environment

– Object location uncertain.– Before contact:

No interaction force.– Unexpected collision

produces only small disturbance force f = k x if k is small.

MURI

Low-LevelControl

Minimum Impedance Control for Grasping and Manipulation

Goal: Build a simple robot gripper that can probe and grasp objects with minimum forces in unstructured environments .

Approach: Combine biologically-inspired elements

• Low-impedance arm

• Minimum impedance controller

• Simple contact sensing

=> Low impedance manipulator arm

Year 1: Implemented testbed system, including hardware, software development system

MURI

Low-LevelControl

Variable Impedance Manipulation Testbed

Whole-Arm Manipulator

(Barrett Technology)• Low moving mass• Minimal friction• Back driveable

MURI

Low-LevelControl

How to Control Robot Motion with Low Stiffness?

Conventional error-based position control law:

Joint torque = = Kp(xd - x) + Kd (vd - v)

– Gain = stiffness: Kp = (torque)/(position change)

– Need high gain Kp for small position error (xd - x)

– If unexpected contact occurs => error (xd - x) becomes large => controller generates large force f ~ Kp(xd - x).

MURI

Low-LevelControl

Model-Based Position Control Law

Joint torque = = arm model + error terms

• Use arm model to generate feedforward torques that make robot follow desired trajectory:

(model) = arm dynamics + joint friction

• Arm model = – Dynamics - inertia, coriolis, gravity, etc.– Friction - each joint– Experimentally measured each term for

the WAM arm testbed

MURI

Low-LevelControl

Model-Based Position Control Law

Joint torque = = arm model + error terms

• Use error terms for minor corrections only

(error) = = Kp(xp - x) + Kd (vp - v)

• If model is accurate, low gains produce good control• Low gains: unexpected contact => only small forces

MURI

Low-LevelControl

Model-Based Position Control Law

Plant

Plant Model

InverseModel

PD

Joint torque = = arm model + Kp(xp - x) + Kd (vp - v)

-

+

xd x

xp

xp-x

More about adaptation in high-level control section

Typical trajectories Position error vs. gain (stiffness)

Without model, error is many times plotted range => Model enables good position control with low gain

MURI

Low-LevelControl

Minimum Impedance Tracking ResultsCommanded path = follow “wedge” at constant velocity

Actual path - low kCommanded path(Actual path, high k)

Y(m)

Low Gain High Gain

Tradeoff betweenimpact force and gain (stiffness)

MURI

Low-LevelControl

Minimum Impedance ControlContact Force Results

Robot probes unknown environment => unexpected contact

Resulting contact force:

Position error vs. gain

Select appropriate gains for task requirements: safety, stability vs. position accuracy

MURI

Low-LevelControl

Minimum Impedance ControlPerformance Tradeoffs

Impact force vs. gain

BioMimetic Robotics

MURIBerkeley-HarvardHopkins-Stanford

MURI

Low-LevelControl

Minimum Impedance ManipulationConclusions and Future Work

• Developed WAM manipulation testbed: Specified & implemented arm and controller, integrated with programming environment

• Created Minimum Impedance Controller, demonstrated superior performance (lower forces) in unexpected contact

BioMimetic Robotics

MURIBerkeley-HarvardHopkins-Stanford

MURI

Low-LevelControl

• Implement simple sensing (force, contact location, vision), integrate with controller to enable manipulation

• Research automatic learning of arm model (cf. Shadmehr)

• Implement impedance learning strategies (cf.

Matsuoka & Howe, Shadmehr)• Build SDM grippers incorporating lessons from

biology, WAM testbed (Full, Shadmehr , Cutkosky)

Minimum Impedance ManipulationConclusions and Future Work

Fabrication

MURILow-LevelControl High-Level

Control

What strategies are used in insect locomotion and what are their implications?Insect locomotion studies (Berkeley Bio)New measurement capabilities (Stanford)

What motor control adaptation strategies do people use and how can they be applied to robots?

Compliance Learning and Strategies for Unstructured Environments (Harvard & Johns Hopkins)Implications for biomimetic robots (Harvard, Stanford)

Guiding questionsGuiding questions

Measurement & Sensing

• Application of micromachined devices for small-scale biological / biomechanical force measurements

1. Adhesion force measurements of single gecko setae• 2-D piezoresistive force cantilever

2. Cockroach ground reaction force measurements• Custom 3-axis force sensor arrays

Structure of a Gecko Foot

(a) (b) (c) (d)

• ~106 setae per animal• Average 4.7 m in diameter• 100-1000 spatulas at tip (~0.2m)• ~20N force per ~200mm2 pad area• Adhesion by van der Waals forces?

2D Piezoresistive Force sensing

Lightly doped(piezoresistive)

Heavily doped(highly conductive)

verticalsensor

lateralsensor

Special 45 ion implantation to embed piezoresistors on surfaces and side walls.

Experiment & Results

1. Pressed down at tip2. Pulled away laterally

Current Progress:• Interpretation of data• Comparison with expected values.

Typical Force CurvesSEM image

Cockroach Carpenter Ant Fruit FlyBlaberus Camponotus Drosophila

Discoidalis Pennsylvanicus Melanogaster

Animal Length 5cm 10mm 2mm

Animal Weight 30mN N 3N

Sensor

Element (5mm)2 (1mm)2 (200m)2

AreaMaximumExpected 300mN 3.5mN 30NForceMinimumResolvable 100N 1N 10nNForce (Typ/50)RequiredSensitivity 1V/N 100V/N 10000V/N(0.1mV/Res.)MinimumMechanical 300Hz 1kHz 3kHzBandwidth

Insect Measurement Requirements

CamponotusPennsylvanicus

1cm

DrosophilaMelanogaster

1mm

Sens

or P

erfo

rman

ceIn

sect

Blaberus Discoidalis

Existing Sensor Design• 64x64 sensor element array, 2x2cm

• On-chip CMOS signal conditioning, amplification, and multiplexing

• Linear dynamic range 0-1.0mN

• Sensitivity– In-Plane: 32V/N

– Normal: 171V/N

• Minimum resolvable load (BW=500Hz) – In-Plane: 3.5N

– Normal: 1N

SensorElements

Wire Bond Pads

Wafer may be diced into stripsby cutting along dashed lines

Substitute

Sensor Array Installation

Sensor Element Design Space

100

200

300

400

500

600

700

800

900

1000

00 50 100 150

Flexure Thickness (m)

Fle

xure

Len

gth

(m

)

Gap 0.5mm

Fail Limit = wt*10*FS = 0.6N

In-Plane Sensitivity 0.1V/N

Normal Sensitivity 1V/N

Power Dissipation 10mW

Other Design Parameters:Flexure Width, w = 100mShuttle Plate Width, ap = 5mm Shuttle Plate Thickness, tp = 0.5mmPiezo/Flexure Fraction, = 0.35Bridge Excitation, Vcc = 15VImplant Dose, Q = 2 x 1013 Ions/cm3

Min. Feature Size, m = 15m

Why these measurements are important• Improve S/N and add multi-axis capability.• Insert MEMS approaches into Locomotion Studies, and mix

Biologists and Engineers• Enable progression towards smaller animals, such as ants

and fruit flies.

Inserting sensors into SDM-manufactured limbs

• There are many sensors distributed throughout roach limbs, although their use in roach locomotion

is not clear.

• SDM enables insertion of “sensing objects”, such as thermometers, strain gauges, and contact

sensors.

• The signals from these sensors must be multiplexed and digitized, and might be reduced to “single-

bit” outputs by comparing with thresholds

• Sensor modules can be built in the form of flexible circuit hybrids, and added to the structure in the middle of SDM

Inserting sensors into SDM-manufactured limbs