Transcript Humanoid systems....II.

Humanoid systems....II.
Motion… notes…
Peter Sincak
&
Maria Virčíkova
Citácia :
1. Çetin Meriçli
Department of Computer Engineering
Boğaziçi University, Turecko,
2. Walking Robots
Sung Hyun Park
BHR Seminar
Types of Locomotion in Nature
Real Robots
Sneak (Epson, Japan)
Rollerwalker (University of Tokyo, Japan)
U-BOT (University of Massachusetts, USA)
Real Robots (cont.)
The Self Deploying Microglider
(EPFL, France)
Aiko
(SINTEF Applied Cybernetics, Japan)
Real Robots (cont.)
Battlefield Extraction-assist Robot
(Vecna Technologies, USA)
Asimo
(Honda, Japan)
Why Legs?
• Potentially less weight
• Better handling of rough terrains
– Only about a half of the world’s land mass is accessible by current man-built
vehicles
• Do less damage to terrains (environmentally conscious)
• More energy-efficient
• More maneuverability
– Use of isolated footholds that optimize support and traction
(i.e. ladder)
• Active suspension
– Decouples the path of body from the path of feet
Why Legs? (cont.)
• Aren’t wheels and caterpillars good enough?
– Wheels and caterpillars always need “continuous” support from the ground.
Legs can enable a robot to make use of “discreet” footholds.
Why Bipeds?
• Why 2 legs? 4 or 6 legs give more stability, don’t they?
– A biped robot body can be made shorter along the walking direction and can
turn around in small areas
– Light weight
– More efficient due to less number of actuators needed
• Everything around us is built to be comfortable for use by human form
• Social interaction with robots and our perception (HRI perspective)
– Form will become as important as functionality in the future
• Our instinctive desire to create a replica of ourselves (maybe?)
Bipedism and Cognition
• Bipedism and cognition has a very close
relationship
– Is it possible to have cognition without locomotion?
– Is it possible to have bipedism without cognition?
HAL 9000
Monkey
Human Evolution
Bipedism frees
the hands to
create tools and
start cognition
Paradox Humanoidných Robotov
Human Evolution vs. Humanoid Evolution
Joints in a Leg
• At least 2 DOF (degrees of freedom) needed to move a leg
– A lift motion + a swing motion
• A human leg has 30 DOF
–
–
–
–
Hip joint = 3 DOF
Knee joint = 1 ~ 2 DOF (almost a hinge)
Ankle joint = 1 DOF (hinge)
24 DOF for the foot!
• In many cases, a robot leg has 3 DOF
– Control becomes increasingly complex with added DOF
• With 4 DOF, ankle joint can be added
• Reasonably walking biped robots have been built with as few as 4 DOF
Joints in a Leg (cont.)
• Picture of a joint model
Stability
• Stability means the capability to maintain the body posture given the
control patterns
• Statically stable walking implies that the posture can be achieved even if
the legs are frozen / the motion is stopped at any time, without loss of
stability
• Dynamic stability implies that stability can only be achieved through
active control of the leg motion
• Statically stable systems can be controlled using kinematic models
• Dynamic walking requires use of dynamical models
Gaits (chodza)
• Gaits determine the sequence of configurations of the legs
– A sequence of lift and release events of individual legs
• Gaits can be divided into 2 main classes
– Periodic gaits  repeat the same sequence of movements
– Non-periodic or free gaits  no periodicity in the control and could be controlled by the
layout of environment
• The number of possible events N for a walking machine with k legs is:
N = (2k – 1)!
• For a biped robot (k = 2), there are 3! = 6 possible events
– Lift left leg, lift right leg, release left leg, release right leg, lift both legs, release both legs
Gaits (cont.)
• An example of a static gait with 6 legs
Gaits and Stability
• People, and humanoid robots, are not statically stable
• Standing up and walking appear effortless to us, but we are actually using
active control of our balance
– We use muscles and tendons
– Robots use motors
• In order to remain stable, the robot’s Center of Gravity must fall under its
polygon of support
– The polygon is basically the projection between all of its support points onto the surface
– In a biped robot, the polygon is really a line
• The center of gravity cannot be aligned in a stable way with a point on that line to keep the
robot upright
Gaits and Stability (cont.)
• Each vertex = support foot
Dot = center of gravity
• Quadruped Robot – Gait Motion
(http://www.youtube.com/watch?v=lxIy3jYuQCo)
Control of a Walking Robot
• 3 things that control must consider for walking:
– Gait: the sequence of leg movements
– Foot placement
– Body movement for supporting legs
• Leg control patterns
– Legs have 2 major states:
•
•
Stance: On the ground
Fly: In the air moving to a new position
– Fly state has 3 major components:
•
•
•
Lift phase: leaving the ground
Transfer: moving to a new position
Landing: smooth placement on the ground
• More DOF for the legs means
– Smoother movement, but
– Increasingly complex controls
Walking vs Running
•
Motion of a legged system is called walking if in all instances at least one leg is
supporting the body
- Honda Asimo walking
(http://www.youtube.com/watch?v=IMR553sg3-Q)
- First Asimo version is E0 in 1986. It took 20-25 seconds for 1 complete step
•
If there are instances where no legs are on the ground, it is called running
- Honda Asimo running
(http://www.youtube.com/watch?v=DZscwdXF920)
- Honda Asimo running (close-up)
(http://www.youtube.com/watch?v=TVSOCb6O-4A)
•
Walking can be statically or dynamically stable
- With 2 legs, almost always dynamically stable
•
Running is always dynamically stable
Biped Walking = Rolling
• Rolling is quite efficient
• Biped walking is similar to rolling a
polygon
– Polygon side length = step length
– As step length gets shorter, more like
rolling a circle
Walking State Methodology
• Walking algorithm for biped robots often derived from classical control
theory
– Uses a reference trajectory for the robot to follow
– Reference trajectories can rarely be defined to work in the real world
•
Irregular terrains and encountering different obstacles, etc.
• Uses static balance poses to define points of tending to balance during a
gait
• The point that a biped robot tends to balance is called a state
• The walking states are chosen as the maximum and minimum tending to
balance stance equilibrium positions where little or no torque needs to be
applied to maintain the state
Walking State Methodology (cont.)
• Marching gait example
• 5 states where the robots tends to either balance or tend to topple
• The center of gravity tends to shift as shown by the cube on top of the
robot
Walking State Methodology (cont.)
• While advancing to new
states during the actual
walking locomotion, an
autonomous robot’s software
should ideally extrapolate the
gait from balanced state to
the next.
Walking State Methodology (cont.)
•
•
•
In states 2 and 4, we can interpret the robot as tending to an out of balance point.
If the leg that is bent continues in the same direction, then the robot will topple.
The control algorithm should not counter the tending to topple position by
bending the other knee on the other leg or shifting the original leg back to its
initial position.
The control algorithm should continue with the balance control state, expecting
that to prevent a fall, the robot has to counter balance by shifting the center of
gravity to either the neutral position or to the next tending to out of balance point
on the opposite side.
Walking State Methodology (cont.)
•
•
•
The velocity and acceleration of the balance control state is determined by the
weight and dynamics of the robot.
All the specific movements pre-determined (hard coded) for each state
Example (Clyon, Florida International University)
(http://video.eng2all.com/clyon-biped-robot/clyon-biped-robotvideo_89396af9e.html)
Passive Walking
• An approach to robotics movement control based on utilizing the gravity
and the momentum of swinging limbs for greater efficiency.
– Conserves momentum
– Less number of actuators
– Natural (anthropormorphic)
• In a purely passive dynamic walking, gravity and natural dynamics alone
generate the walking cycle
– Active input is necessary only to modify the cycle, as in turning or changing speed
• Examples
– 3 legs (http://www.youtube.com/watch?v=fdN0_LO-vCY)
– 2 legs (http://www.youtube.com/watch?v=CK8IFEGmiKY)
Zero Moment Point (ZMP)
• Introduced in 1968 by Miomir Vukobratovic
• Specifies the point with respect to which dynamic reaction force at the
contact of the foot with the ground does not produce any moment (i.e.
the point where total inertia force equals 0)
• Assumes the contact area is planar and has sufficiently high friction to
keep the feet from sliding (no sliding assumption)
• The trajectory is planned using the angular momentum equation to ensure
that the generated joint trajectories guarantee the dynamical postural
stability of the robot, which usually is quantified by the distance of the
zero moment point in the boundaries of a predefined stability region.
Zero Moment Point (ZMP) (cont.)
• Ground reaction force and ZMP are generally measured with a
series of sensors embedded in the feet
– Pressure sensitive transducers, foot switches, strain gage based sensors,
force sensitive resistors, and novel force-torque transducers
Zero Moment Point (ZMP) (cont.)
• Center of pressure (CoP) is a ground reference point where the
resultant of all ground reaction forces acts
– At this point, it is assumed that all of the forces that act between the body
and the ground through the foot can be simplified to a single ground
reaction force vector and a free torque vector
– If the horizontal forces between the feet and the ground can be neglected,
then the CoP can be defined as the centroid of the vertical force distribution
Zero Moment Point (ZMP) (cont.)
Zero Moment Point (ZMP) (cont.)
• For flat horizontal ground surfaces, ZMP == CoP
• At any point P under the robot, the reaction can be represented
by a force and a moment Mgrf
Zero Moment Point (ZMP) (cont.)
• Around the ZMP (localized at rzmp ) the moment around the
horizontal axis are zero and there is only a component of
moment around the vertical axis
• The resulting moment of force exerted from the ground on the
body about the ZMP is always around the vertical axis
• At the ZMP is a reference point at the ground in which the net
moment due to inertial and gravitational forces has no
component along the (horizontal) axes (parallel to the ground)
• The trajectory that the ZMP follows is utilized and planned such
that they are within the supporting polygon defined by the
location and shape of the foot print
Zero Moment Point (ZMP) (cont.)
• Anyways, in a very brief summary…
Zero Moment Point (ZMP) (cont.)
• Anyways, in a very brief summary…
Zero Moment Point (ZMP) (cont.)
•
Honda’s Asimo
(http://www.youtube.com/watch?v=VTlV0Y5yAww&feature=PlayList&p
=85F8464A742759D1&playnext=1&index=5 )
•
AIST’s HRP-2
(http://www.youtube.com/watch?v=iigiFYzwjjE )
•
AIST’s HRP-3
(http://www.youtube.com/watch?v=gO9th_Rfk2o )
Sources (cited within this presentation)
•
Robot Locomotion by Henrik Christensen (http://www.nada.kth.se/kurser/kth/2D1426/slides2006/autrob2-2up.pdf )
•
Walking Robots and Especially Hexapods by Marek Perkowski
(http://web.cecs.pdx.edu/~mperkows/CLASS_479/May6/024.walking-robots-design.ppt#8 )
•
Estimation of ground reaction force and zero moment point on a powered ankle-foot prosthesis by
Martinez Villalpando and Ernesto Carlos (http://dspace.mit.edu/handle/1721.1/37271 )
•
Design of a Biped Robot by Andre Senior and Sabri Tosunoglu
•
Overview of ZMP-based Biped Walking by Shuuji Kajita
(http://www.dynamicwalking.org/dw2008/files/presentations/DW2008_keynotepresentation_Shuuji_Kaji
ta.pdf )
•
www.wikipedia.org (on ZMP)
Challenges in Humanoids
• Bipedal human-like locomotion
– Stable gait
• Changing model during one/two feet support walking
• Two legs, two arms, head, torso
• Hyper DOF system (>20)
– Complex kinematics and dynamics
• Complex real-time control architecture
Humanoid Evolution (cont’d)
• Nowadays, humanoid robot researchers are
focusing on bipedism more than they do in
cognition
• Stable and robust bipedal locomotion is still a
good lab example
• It is mandatory to solve it in order to be able
to implement cognition
• We are in the pre-robotic era compared with
the human evolution
Bipedal Locomotion
•
ZMP (Zero Moment Point)
specifies the point with
respect to which dynamic
reaction force at the contact
of the foot with the ground
does not produce any
moment, i.e. the point
where total inertia force
equals 0 (zero).
• ZMP is the indicator of the
stability of the robot:
– if it is in the foot shadow –
stable,
– İf not – unstable.
• The shadow depends on
single or double support
phase.
Active vs. Passive Locomotion
• Common humanoid uses all their DOF to perform
the movement:
– Continuous motor consumption (including arms)
– Continuous motor control and synchronization
– Extremely complex real-time control
• How is possible to reduce complexity?
– Reducing number of active DOF
– Using DOF only when it is strictly necessary
– Using energy of previous step to generate the next
– These actions reduce also the consumption
Passive Dynamic Walking
• Human walking strategy:
– Let their legs swing as they would on their own,
– Then add a little control and power, yielding a gait
with inherently low energetic and control demands.
• Advantages:
– In contrast to rigidly joint-controlled robots, walking
robots based on passive-dynamic principles can
have human-like efficiency and actuation
requirements.
• Disadvantages:
– Movements are mostly in sagital plane and in
straight line, being extremely difficult to turn, go
back, seat,etc. The motion is mostly symmetrical.
Passive Dynamic Walking (cont’d)
• Active Gait : Always stable
• Passive Gait : Sometimes unstable
Záver.....
• Kinematika .. Veľmi komplexný systém a stale
problém – zdá sa že Honda a Asimo je najďalej
v tejto oblasti....
http://www.youtube.com/watch?v=Bmglbk_Op
64
http://fora.tv/2009/05/30/Rodney_Brooks_Rem
aking_Manufacturing_With_Robotics
Seminár KC....
Clouds and Robotics.... Vedecka kaviareň
15.00 – dnes.. miesto cvika…...
))