Forschung 

High performance locomotion
The speed of walking robots is a critical factor in many applications,
especially in autonomous robot soccer games. Our aim is to increase the
walking speed of our robots through automated hardwareintheloop
optimization.
Many different approaches have been investigated for improving the walking speed
of bipedal and quadrupedal robots. For example, numerical optimal control
techniques
enable the computation of stable and fast walking motions based on three
dimensional
computational models of the legged robot dynamics. However, all modelbased
optimization
approaches have in common that their outcome critically depends on the quality
and
accuracy of the robot model. The derivation of highly accurate enough robot
models to
achieve the best possible walking speed may require too many efforts
considering, e.g.,
the effects of gear backlash, elasticity and temperature dependent joint
friction or of different
ground properties. An alternative approach is to start with a reasonable,
initial
walking motion and then to use online hardwareintheloop optimization of the
physical
robot. For example, reinforcement learning techniques for fourlegged and
biped robots
have successfully been used to improve fast and stable walking motions, where
a (low
dimensional) parametrization of the walking motion is utilized to optimize the
gait by
evaluating the merit function performing walking experiments with the real
robot.
In the following our current work on quadrupedal (videos) and
bipedal (videos) walking
optimization is described briefly.
Optimization for our robots of type Sony Aibo was performed in two scenarios,
(1) straight foward walking
without ball, (2) turning around (it's vertical axis) without
losing the ball, whereby the ball is placed in between the front
legs of the robot, and it has to stay there during the turning
motion. These two cases often arise during a robot scoccer
game, the first when the robot has to reach the ball, the second
one when it is aligning for passing the ball to another robot, or
when it tries to shoot a goal.
The walking trajectories of the four legs are determined in space
and time by a 31 dimensional parameter vector. This low dimensional
parametrization is achieved by approximating the trajectory by polygons
and by taking advantage of symmetry and redundancies
of a four legged walking robot. The walking optimization is
performed on the standard RoboCup 4legged
league field, where the walking or turning speed of the robot is
measured by a ceiling camera. The position and orientation
of the robot is detected using circular markers attached to the
robot's back. The speed of the robot is then approximated from
two consecutive measurements over a fixed time interval.
The average speed measured over a fixed number of runs is
returned to the optimizer as the objective function value. When optimizing
turning while holding the ball, a distance sensor in the chest of the
robot is applied in order to determine whether the ball was lost while
walking. For such an unsuccessful run, an evaluation value of zero is
returned to the optimizer. Therefore walking motions are penalized
where the ball is not held securely.
This way a hardwareintheloop layout is applied, where the
robot control is coupled with the optimization tool, in our case
APPSPACK (ver. 4.0.2). This method is originally
designed for deterministic nonlinear optimization problems,
but for the here considered problem with
averaged walking speed respectively turning speed the
chosen optimization method performed well. Besides the restricting
factor in this application is the number of walking experiments,
so that no implemented stopping criteria of APPSPACK is used,
which would also be disturbed by the underlying stochastic character
of this application.
The starting parameter set for the forward walking speed
optimization is obtained by an optimization experiment with an
evolutionary algorithm, for the turning speed optimization we
start with a hand tuned initial parameter set. Beside of bounds
for the parameter space representing reachable leg positions, we
are using the default parameters of APPSPACK.
For walking forward the measured speed of the initial parameter
set was 40 cm/s. The resulting parameters obtained by optimization
using APPSPACK was measured at an average speed of 43 cm/s.
Thus yielding an improvement in the walking speed of about 7.5%.
This result was achieved after evaluating 83 parameter sets in about half an
hour
optimization time. When optimizing the turning motion of the robot,
the turning speed could be increased about 50% from 120 deg/s to
180 deg/s without losing the ball while turning. Finding this result
required 206 parameter set evaluations which took about 45 minutes.
The resulting walking motions can be observed in two short videos.
One demonstrating forward walking,
the other demonstrating turning with
grabbed ball. The robot on top is performing the optimized motion, the robot
below is using the initial parameter set.
Walking optimization of humanoid robots
The walking motion of our humanoid robots
is generated by prescribing trajectories for the hip and the feet and solving
the inverse
kinematics for the joint angles. Thus the walking motion is parameterized by a
large
number of degrees of freedom for the trajectories of hip and feet. By
parameter tuning
by hand the most important parameters have been identified. Those are the
relation of
the distances of the front and of the rear leg to the center of mass, the
lateral position,
the roll angle and the height above ground of the foot during swing phase, and
the pitch
of the upper body.
As
quality or objective function value of a walking motion we measure the
distance the robot
covers, when it starts walking with a small step length and increases it
linearly during
the experiment until the robot falls or reaches a final step length.
During the numerical online optimization the realvalued parameters
influencing the
main characteristics of the walking behavior of the robot are varied to
maximize the
defined objective function. The robot is included as hardwareintheloop for
the walking
experiment to evaluate the objective function. In this context a non
deterministic
blackbox optimization problem arises, where besides of a noise function value
no further
information, especially no objective gradients, is provided. By this
definition of the objective
function, it is not necessary to formulate additional constraints for
maintaining
walking stability and incorporate them explicitly into the definition of the
optimization
problem. The only constraints to be considered are the constant lower and
upper bounds
on the optimization parameters.
We start the optimization process with one experiment, where the parameters
are
chosen by expert knowledge to provide a stable, initial walking motion (video).
An
initial set of
experiments is generated around the initial motion by varying each parameter
on its own.
This set builds the basis points for the use of design and analysis of
computer experiments,
which is applied to approximate the original objective function on the whole
feasible parameter
domain.
A sequential quadratic programming method is applied next to compute
the maximizer of the resulting surrogate function.
The objective function
value for this maximizer is determined
by performing the
corresponding walking experiment with the robot.
If the distance of a found maximizer to a point already evaluated by
experiments
falls below a defined limit, not the actual maximizer, but the maximizer of
the expected
mean square error of the surrogate function is searched, evaluated, and added
to the set
of basis points for approximation.
This procedure improves the approximation quality
of the surrogate function in unexplored regions
of the parameter domain and provides
not to get stuck in a local maximum.
After a new point is added, a new surrogate
function
is approximated, and the optimization starts again. From our experience this
approach
for online optimization of walking speed is much more ecient then genetic or
evolutionary
algorithms which are usually applied to cope with the robust minimization of
noisy
functions.
The solutions found during the optimization were successively adjusted by
setting up
a constant step length and step time dependent on different floor coverings. The
results
of this approach are a stable walking motion with a speed of 30
cm/sec for the HR18 robot prototype
(video) and a stable walking
motion with a speed of more than 40 cm/sec for an improved hardware
design of the HR18 robot ("Bruno") (video),
which is so far the fastest motion reported for a humanoid robot in the kid
size league of
RoboCup.
