Biomimetic Robots for Robust Operation in Unstructured Environments

1
Biomimetic Robots for Robust Operation in
Unstructured Environments

• University of California at Berkeley
• H. Kazerooni

2

• The objective is to create robust and small
  walking machines.
• (Insects are good examples because they are small
  and robust.)

3

• 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.

4
WALKING
ROUGH TERRAIN
SMOOTH TERRAIN
Single D.O.F (Mechanism)
Multiple D.O.F (Complex Machine)

• 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

Walking Speed is independent of load -- i.e.
students walk at the same speed regardless of
their backpack load
5
You Stop Growing (L Constant)
6
Running
ROUGH TERRAIN
SMOOTH TERRAIN
Single D.O.F (Mechanism)
Multiple D.O.F (Complex Machine)
7
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.

8

• 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

9

• 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.

10
The Classical Robotics technology may not be
effective in design of small and robust walking
machines
11
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)

12
Human foot Trajectory
13
Cockroach Foot Trajectory(Cutesy of Bob Full
Lab)
14
Design of Mechanism to Mimic Cockroach Leg
Trajectory
15
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.

16
Verification of Trajectories
17
Experimental Machine at UC-Berkeley
size 3.5"x3" speed 3 inch/sec
18
(No Transcript)
19
Guiding questions
What passive properties are found in Nature?
Preflexes Muscle and Exoskeleton Impedance
Measurements (Berkeley Bio.)
What properties in mechanical design?
Biological implications for Robotics Basic
Compliant Mechanisms for Locomotion
(Stanford) Variable compliance joints (Harvard,
Stanford) Fast runner with biomimetic trajectory
(Berkeley ME)
Fabrication
How should properties be varied for changing
tasks, conditions ?
Matching ideal impedance for unstructured dynamic
tasks (Harvard)
20
Minimum Impedance Control
BioMimetic Robotics MURI Berkeley-Harvard Hopkins
-Stanford

• Minimizing Interaction Forces in Exploration and
  Manipulation

Jaydev P. Desai and Robert D. Howe Harvard
University
21
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.

Key robot capability for unstructured environments
22
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

23
Variable Impedance Manipulation Testbed

• Whole-Arm Manipulator
• (Barrett Technology)
• Low moving mass
• Minimal friction
• Back driveable

gt Low impedance manipulator arm
Year 1 Implemented testbed system, including
hardware, software development system
24
How to Control Robot Motion with Low Stiffness?

• Conventional error-based position control law
• Joint torque t 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 gt error (xd -
  x) becomes large gt controller
  generates large force f Kp(xd - x).

25
Model-Based Position Control Law

• Joint torque t arm model error terms
• Use arm model to generate feedforward torques
  that make robot follow desired trajectory
• t(model) arm dynamics joint friction
• Arm model
• Dynamics - inertia, coriolis, gravity, etc.
• Friction - each joint
• Experimentally measured each term for the WAM
  arm testbed

26
Model-Based Position Control Law

• Joint torque t arm model error terms
• Use error terms for minor corrections only
• t(error) Kp(xp - x) Kd (vp - v)
• If model is accurate, low gains produce good
  control
• Low gains unexpected contact gt only small forces

27
Model-Based Position Control Law
Joint torque t arm model Kp(xp - x) Kd
(vp - v)
Plant Model
xp
Inverse Model
x
xd
Plant

PD
-
xp-x
More about adaptation in high-level control
section
28
Minimum Impedance Tracking Results
Commanded path follow wedge at constant
velocity
Actual path - low k Commanded path(Actual path,
high k)
Y(m)

• Typical trajectories

Position error vs. gain (stiffness)
Without model, error is many times plotted range
gt Model enables good position control with low
gain
29
Minimum Impedance ControlContact Force Results

• Robot probes unknown environment gt unexpected
  contact

Resulting contact force
Low Gain
High Gain
30
Minimum Impedance ControlPerformance Tradeoffs
Position error vs. gain
Impact force vs. gain
Select appropriate gains for task requirements
safety, stability vs. position accuracy
31
Minimum Impedance ManipulationConclusions and
Future Work
BioMimetic Robotics MURI Berkeley-Harvard Hopkins
-Stanford

• 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

32
Minimum Impedance ManipulationConclusions and
Future Work
BioMimetic Robotics MURI Berkeley-Harvard Hopkins
-Stanford

• 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)

33
Guiding questions
What strategies are used in insect locomotion and
what are their implications?
MURI
Low-Level Control
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?
Fabrication
Compliance Learning and Strategies for
Unstructured Environments (Harvard Johns
Hopkins) Implications for biomimetic robots
(Harvard, Stanford)
34
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

35
Structure of a Gecko Foot

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

36
2D Piezoresistive Force sensing
Special 45? ion implantation to embed
piezoresistors on surfaces and side walls.
37
Experiment Results
SEM image
Typical Force Curves

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

1. Pressed down at tip 2. Pulled away laterally
38
Insect Measurement Requirements
Insect
Sensor Performance
Blaberus Discoidalis
39
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 (BW500Hz)
• In-Plane 3.5mN
• Normal 1mN

40
Sensor Array Installation
41
Sensor Element Design Space
Other Design Parameters Flexure Width, w
100mm Shuttle Plate Width, ap 5mm Shuttle
Plate Thickness, tp 0.5mm Piezo/Flexure
Fraction, g 0.35 Bridge Excitation, Vcc
15V Implant Dose, Q 2 x 1013 Ions/cm3 Min.
Feature Size, m 15mm
42
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.

43
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

44
Inserting sensors into SDM-manufactured limbs

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