Safe fall: Humanoid robot fall direction change through intelligent stepping and inertia shaping
ABSTRACT Although fall is a rare event in the life of a humanoid robot, we must be prepared for it because its consequences are serious. In this paper we present a fall strategy which rapidly modifies the robot's fall direction in order to avoid hitting a person or an object in the vicinity. Our approach is based on the key observation that during “toppling” the rotational motion of a robot necessarily occurs at the leading edge or the leading corner of its support base polygon. To modify the fall direction the robot needs to change the position and orientation of this edge or corner visavis the prohibited direction. We achieve it through intelligent stepping as soon as a fall is detected. We compute the optimal stepping location which results in the safest fall. Additional improvement to the fall controller is achieved through inertia shaping techniques aimed at controlling the centroidal inertia of the robot. We demonstrate our results through the simulation of an Asimolike humanoid robot. To our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.

Dataset: IR0320140311
 SourceAvailable from: SungHee Lee
Conference Paper: Fall on Backpack: Damage Minimizing Humanoid Fall on Targeted Body Segment Using Momentum Control
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ABSTRACT: Safety and robustness will become critical issues when humanoid robots start sharing human environments in the future. In physically interactive human environments, a catastrophic fall is the main threat to safety and smooth operation of humanoid robots, and thus it is critical to explore how to manage an unavoidable fall of humanoids. This paper deals with the problem of reducing the impact damage to a robot associated with a fall. A common approach is to employ damageresistant design and apply impactabsorbing material to robot limbs, such as the backpack and knee, that are particularly prone to fall related impacts. In this paper, we select the backpack to be the most preferred body segment to experience an impact. We proceed to propose a control strategy that attempts to reorient the robot during the fall such that it impacts the ground with its backpack. We show that the robot can fall on the backpack even when it starts falling sideways. This is achieved by utilizing dynamic coupling, i.e., by rotating the swing leg aiming to generate spin rotation of the trunk (backpack), and by rotating the trunk backward to drive the trunk to touch down with the backpack. The planning and control algorithms for fall are demonstrated in simulation.ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference; 01/2011  SourceAvailable from: YeounJae Kim
Dataset: IR0320140311
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Safe Fall: Humanoid robot fall direction change
through intelligent stepping and inertia shaping
Seungkook Yun
Massachusetts Institute of Technology
Cambridge, MA 02139
U.S.A.
yunsk@mit.edu
Ambarish Goswami
Honda Research Institute
Mountain View, CA 94041
U.S.A.
agoswami@hondari.com
Yoshiaki Sakagami
Honda Research Institute
Mountain View, CA 94041
U.S.A.
ysakagami@hondari.com
Abstract—Although fall is a rare event in the life of a humanoid
robot, we must be prepared for it because its consequences are
serious. In this paper we present a fall strategy which rapidly
modifies the robot’s fall direction in order to avoid hitting a
person or an object in the vicinity. Our approach is based on
the key observation that during “toppling” the rotational motion
of a robot necessarily occurs at the leading edge or the leading
corner of its support base polygon. To modify the fall direction the
robot needs to change the position and orientation of this edge or
corner visavis the prohibited direction. We achieve it through
intelligent stepping as soon as a fall is detected. We compute
the optimal stepping location which results in the safest fall.
Additional improvement to the fall controller is achieved through
inertia shaping techniques aimed at controlling the centroidal
inertia of the robot.
We demonstrate our results through the simulation of an
Asimolike humanoid robot. To our knowledge, this is the first
implementation of a controller that attempts to change the fall
direction of a humanoid robot.
Index Terms—humanoid robot fall, safe fall, fall direction
change, support base geometry, inertia shaping
I. INTRODUCTION
Safety is a primary concern that must be addressed before
humanoid robots can freely exist in human surroundings. Out
of a number of possible situations where safety becomes
an issue, one that involves a fall is particularly worrisome.
Fall from an upright posture can cause damage to the robot,
to delicate and expensive objects in the surrounding or to
a human being. Regardless of the substantial progress in
humanoid robot balance control strategies, the possibility of
a fall remains real, even unavoidable. Yet, a comprehensive
study of humanoid fall and prescribed fall strategies are rare.
A humanoid fall may be caused due to unexpected or
excessive external forces, unusual or unknown slipperiness,
slope or profile of the ground, causing the robot to slip, trip
or topple. In these cases the disturbances that threaten balance
are larger than what the balance controller can handle. Fall
can also result from actuator, power or communication failure
where the balance controller is partially or fully incapacitated.
In this paper we consider only those situations in which the
motor power is retained such that the robot can execute a
prescribed control strategy.
A fall controller can target two major objectives indepen
dently or in combination: a) fall with a minimum damage
and b) change fall direction such that the robot does not hit
a certain object or person. The present paper introduces a
strategy for fall direction change and describes a controller
which can achieve both objectives.
Fig. 1 shows two cases of a fall caused by a frontward push
on an upright standing humanoid robot (top figure). Without
any fall controller, the robot falls forward and hits a block
located in front of it (bottom, left). In the second case (bottom,
right), the robot takes cognizance of the position of the block
and the proposed controller successfully avoids hitting it.
Fig. 1.
controller. The robot is initially in upright pose and is subjected to a forward
push (top) shown by the green arrow. Without any fall controller the robot
falls on the block object in front of it (bottom, left), potentially damaging or
breaking it. For the same push, the fall controller successfully changes the
fall direction and the robot is able to avoid hitting the object (bottom, right).
Consequence of a humanoid fall without and with the proposed fall
Let us note that a fall controller is not a balance controller.
A fall controller complements, and does not replace, a bal
ance controller. Only when the default balance controller has
failed to stabilize the robot, the fall controller is activated.
Further, a fall controller is not a pushrecovery controller.
A pushrecovery controller is essentially a balance controller,
which specifically deals with external disturbances of larger
magnitude. A robot can recover from a push e.g., through an
appropriate stepping strategy[12].
2009 IEEE International Conference on Robotics and Automation
Kobe International Conference Center
Kobe, Japan, May 1217, 2009
9781424427895/09/$25.00 ©2009 IEEE 781
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II. BACKGROUND AND KEY CONCEPTS
In this section we will review the existing literature and
introduce some of the key concepts used throughout the paper.
A. Related work
Work on humanoid fall is rare. Fujiwara et al. at the Japan
AIST Laboratory has done major work in the area of fall
control of humanoid robots [7], [2], [3], [6], [4], [5]. Although
these papers are concerned with the impact minimization
problem, some important points of general applicability are
established. [3] points out the advantages of a simulator for
fall control research and [4] reports the design and building
of a dedicated hardware (robot) for fall study.
In another work by Ogata et al., a fall detection condition
based on the “degree of abnormality” to distinguish between
fall and nofall conditions was proposed [11], [10]. The robot
improves fall detection through learning. We have also used
a somewhat similar concept, which we call the Fall Trigger
Boundary (FTB) and is described next.
B. Fall trigger boundary (FTB)
As shown in Fig. 2 the FTB encloses a region in the robot’s
feature space in which the balance controller is able to stabilize
the robot. An exit through the FTB is an indication of a certain
fall and this event is used to activate a switch from the robot’s
balance controller to a fall controller. The parameters that
characterize the feature space can include both sensor data
from and any number of computed variables such as center of
mass (CoM) and center of pressure (CoP) positions, robot lean
angle, angular momentum, etc. The shape and size of the FTB
depends on the nature of the balance controller. Wieber [16]
proposed a similar concept as viability kernel which tracks
all the states as joint angles and velocities of a humanoid
that adapts its motion according to the kernel. We focus not
so much on the interior of the kernel but on the boundary
between balanced and unbalanced regions.
FTB
v1
v2
v3
Balance
control
Certain Fall
Triggers fall control
Fig. 2. Schematic of Fall Trigger Boundary (FTB), a boundary in a humanoid
feature space that surrounds the region where the humanoid is able to maintain
balance. The axes in the figure represent different robot features such as CoM
coordinates, angular momentum components, etc. The FTB represents the
limit beyond which the robot controller must switch to a fall controller.
C. Support base geometry modification
The direction of fall of a humanoid robot is fully determined
by the CoP location with respect to the support base. The
support base can be approximated by a polygonal area which
is the convex hull of all the contact points between the robot
feet and the ground. When the robot starts to topple, its CoP
touches an edge (or corner) of the support base. The robot
rotates about this leading edge (corner). Therefore, a change
in the physical location of the leading edge (corner) of the
support base with respect to the robot CoM exerts influence
on the direction of robot rotation, i.e., the direction of fall.
In Fig. 3 a humanoid robot is subjected to a forward push
as indicated by the red arrow. If the push is strong enough to
topple the robot, the CoP will approach the front edge (red)
of the support base and the robot will begin to rotate about
this leading edge.
P
Q
T
P
Q
T
Fig. 3.
change through support base geometry modification. A forward push is
assumed. P denotes the CoP, and Q is the reference point (explained in
text). The dotted lines show the support base (polygonal convex hull) of the
robot while the polygon edge containing CoP is red dotted.
A schematic diagram showing the basic idea behind fall direction
The direction and magnitude of the toppling motion is
given by PQ where P is the CoP and Q is what we call
a reference point. The reference point indicates the direction
and magnitude of fall. In this paper we have used the capture
point[12] as our reference point1. Although PQ may not be
initially perpendicular to the leading edge of support base, it
becomes so once the toppling motion sets in.
With a different geometry of the support base as in Fig. 3(b),
for the same push, the robot would rotate about a new leading
edge and in the new direction PQ. If the robot is to avoid
falling on an object in front of it, we can effect a change in
the fall direction by changing the support base (specifically,
its leading edge) from Fig. 3(a) to Fig. 3(b).
There are two major challenges that we face. First, the
robot becomes underactuated as soon as it starts toppling.
This creates 1 or 3 additional dofs depending on whether
the robot is toppling about an edge or a corner. Therefore,
we should design a controller very carefully to deal with this
underactuated phase. Second, the CoP and the reference point
continuously move as the robot moves.
The proposed control strategy can be implemented accord
ing to the following steps:
1We will further describe capture point in Section IIIA2.
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1) Compute control duration, the length of time after the
push force has disappeared, during which the controller
is assumed to be active.
2) Estimate reference point location at the end of control
duration, based on inverted pendulum model.
3) Compute optimal stepping location on the ground.
4) Control humanoid legs to step to optimal location.
5) Employ inertia shaping to generate angular momentum
that further diverts the robot away from the obstacle.
III. SUPPORT BASE GEOMETRY MODIFICATION
CONTROLLER
Once the humanoid state exits the fall trigger boundary, the
fall controller estimates the direction and timetofall of the
robot. Based on the estimation, it computes the best position
to step and controls the leg accordingly.
The proposed approach sequentially employs two con
trollers, the support base geometry modification and inertia
shaping, to change the direction of fall. This section describes
the first controller.
A. Robot state estimation through simple humanoid models
To speed up calculations for predicting the robot states we
approximate the robot with an equivalent inverted pendulum,
see Fig. 4. The pendulum connects the CoP and CoM of the
robot and has a point mass equal to the robot mass. If the CoP
is located on an edge of the support base, we model the robot
with a 2D inverted pendulum. If instead, the CoP is located
at a corner, the estimation uses a 3D spherical pendulum
model. The 2D pendulum model has a closedform solution.
However, since the spherical pendulum does not have closed
form solutions, we simply simulate its dynamic equations for
the period of control duration. Because the control duration is
typically very short, this simulation can be adequately handled.
1) Estimation of the control duration: Timetofall is a
critical parameter for the evaluation and formulation of a fall
response strategy. The biomechanics literature gives us a few
data on the timetofall of human. A simple forward fall of
an adult starting from a stationary 15◦inclination takes about
0.98s, whereas that for a backward fall starting from stationary
5◦inclination takes 0.749s (for a flexed knee fall) and 0.873s
(for an extended knee fall)[14].
The fall controller remains active until the lean angle θ
between the humanoid CoPCoM line and the vertical axis
crosses a certain threshold θthreshold. We assume that all
external forces have disappeared when the robot starts to use
the fall controller. The control duration is obtained through
an incomplete elliptic integral of the first kind of the 2D
pendulum model [13] when the lean angle goes over the
threshold. For the spherical pendulum model, we simulate its
dynamic equations.
2) Estimation of reference point: As mentioned before, we
have used the capture point as the reference point in this work.
Capture point is the point on the ground where a humanoid
must step to in order to come to a complete stop after an
external disturbance[12]. The location of the capture point is
proportional to the linear velocity of the robot’s CoM. Capture
mg
P
threshold
Fig. 4.
CoP, m is the humanoid mass, and θ is the lean angle between the CoPCoM
line and the vertical. We use this model for the fast estimation of time duration
and other parameters of the robot.
Simple model of an inverted pendulum falling under gravity. P is
point (xQ, yQ) for an inverted pendulum approximation of the
robot is computed as follows:
xQ= xG+
?zG
?zG
g
˙ xG
(1)
yQ= yG+
g
˙ yG
(2)
where (xG,yG,zG) and (˙ xG, ˙ yG, ˙ zG= 0) are the robot CoM
position and velocity, as estimated from the dynamic equations
of the pendulum models.
Suppose the control duration is ΔT. In the 2D pendulum
model, the velocity after ΔT is computed from the energy
equation as follow:
?
I
˙θ(ΔT) =
2E
−2mgLcos(θ(ΔT))
I
(3)
where E is the total energy (constant) of the pendulum, I is
the moment of inertia with respect to CoP and L is the distance
between CoP and CoM. Simulation of the dynamic equations
yields the velocity of the spherical pendulum.
B. Definition of the optimal step location
Fig. 5 shows a robot that is about to topple. The old CoP P1
has reached an edge of the support base, and the support base
has shrunk to a line. Approximating the robot as a rigid body
instantaneously, the trajectory of the CoM is parallel to P1Q1.
T is the target object to avoid. Our goal is to find a point P2
within the allowable stepping zone of the robot such that the
robot is maximally diverted away from T, i.e., to maximize
α2.
Assuming that the humanoid is in double support phase, the
optimal CoP is selected among the following 5 cases:
1) No change, i.e., robot does not react
2) (2 cases) Lifting (and not replanting) left or right foot
3) (2 cases) Taking left or right step
We use a bruteforce search for each case to find the
optimal new CoP. The allowable stepping zone on the floor
where the robot’s swing foot can reach within the control
duration is denoted by D, shown as the green dotted polygon
in Fig. 5. This area is divided into cells of x, y and β, the
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P1
P1
P2
P2
Q1
Q1
TT
?1
?1
?2
?2
Q2
Q2
Fig. 5.
and Q1is the reference point when the controller starts, and the robot will fall
in the direction P1Q1. T is the target to avoid. The support base is shown in
blue dotted lines. The green dotted lines enclose the allowable stepping zone,
the region where a new CoP can be placed through proper foot placement.
P2 is a candidate for the new CoP by stepping. Q2 is the estimated new
reference point when the step is taken. αi is the avoidance angle between
PiQiand PiT. The fall controller will try to maximize α2.
Schematic of a biped robot subject to a fall. P1is the current CoP,
angular displacement (of foot). We relocate the nonsupport
foot according to (x,y,β) of each cell, and estimate a new
reference point and CoP.
The avoidance angle α is computed for each case and the
optimal CoP is selected as follows:
P2= argmax
P2∈CoP(D)
angle(Q2P2T)
(4)
We assume a rectangular foot sole and the support polygon
can be computed with a finite number of points. The reference
point needs to be estimated at the time the nonsupport foot
touches the ground.
C. Estimation of the allowable stepping zone
Given the control duration, the allowable stepping zone is
estimated using leg Jacobians. Suppose the robot has a firm
support on the ground. With two legs, we have the following
equations:
˙PL−˙Pbody= JL˙θL
˙PR−˙Pbody= JR˙θR,
(5)
(6)
where PLand PRare the positions of the left and right feet,
respectively, with respect to the support foot PL, and Pbody
is the location of the body frame, θLand θRare 6 × 1 joint
angle vectors of the robot legs, and JL and JR are the leg
Jacobian matrices.
Performing (Eq. 6)(Eq. 5):
˙PR−˙PL= [JR − JL]
?˙θR˙θL
?T
(7)
where we have used the (6×12) foottofoot Jacobian matrix
asˆJ = [JR
− JL].
The size of the allowable stepping zone is estimated by the
Jacobian as shown in Fig. 6.
Dk(x,y,β)= ΔT
12
?
i=1
???ˆJki˙θMAX
i
??? ≈ γΔT
12
?
i=1
???ˆJki
???
(8)
?
?
??
??
??
?
Fig. 6.
left foot is the support foot. P is the CoP with the single support and Q is the
reference point. The allowable stepping zone is the upper part of the rectangle
(above the blue separatrix) with edges Dx and Dy. Dβdenotes amount of
rotation of the swing foot.
In this figure the allowable stepping zone is shown in yellow. The
where˙θMAX
constant included to approximate˙θMAX
same for all joints.
We use only the upper half of the region cut by the
separatrix line which is perpendicular to PQ and goes through
the center of the moving foot. This is because a robot that is
falling along PQ can hardly place its foot on the other side.
i
is the maximum velocity of a leg joint. γ is a
i
, which is assumed
D. Step controller for a toppling humanoid
Once the optimal step location is computed one can expect
to simply control the joint angles through an invers kinematics.
However, taking a successful step to the optimal step location
is not trivial because inverse kinematics solution will not be
precise for a toppling robot. The main problem is that the
support foot of the robot is not flat with the ground, i.e., it is
underactuated, and robot is not likely to step as expected.
To compensate for this we need to know the foot rotation
angle of the robot. Assuming that the robot possesses a gyro
sensor in the trunk, the foot rotation angle can be estimated
by noting the mismatch between the trunk orientation angle
as computed by the gyro and by the robot joint angle sensors.
With this information, we implement a leg controller to ensure
that the swing foot is flat as it touches down on the ground.
Since we assume that the CoP does not change during fall,
the CoP is modeled as passive rotational joint at which the
support foot rotates, as shown in Fig. 7.
P
Q
Ts
Tn
P
Ts
Tn
Fig. 7.
CoP, P, towards the reference point Q. We model a free joint at P. Without
external forces, the joint angle should increase. (right) desired landing posture
(Left) For a robot that is falling, the support foot rotates about the
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Although we cannot actuate the passive joint, the support
foot is expected to rotate as estimated in the pendulum models
without control. A transformation matrix from the support foot
to the nonsupport foot Ts
nis:
Ts
n= T0
s
−1T0
n
(9)
where T0
already fixed in optimizing the CoP.
If the joints are controlled according to Ts
support foot hits the ground as in Fig. 7 (Left), the robot is
expected to step on the desired location by gravity, Fig. 7
(Right). Note that the precomputed T0
posture, Fig. 7(b).
We can compute joint velocities to move the swing leg as.
nis the transformation of the nonsupport foot and
nbefore the non
sis for the landed
˙θ =ˆJ
#(˙PR−˙PL)
(10)
whereˆJ
#is the least dampedsquare pseudoinverse ofˆJ.
IV. WHOLEBODY FALLING MOTION CONTROL THROUGH
INERTIA SHAPING
The humanoid can attempt to change the fall direction
further, after the step is taken. Since a falling robot is nor
mally underactuated, direct control of the CoM would not be
effective. However, we can indirectly change the fall direction
by generating angular momentum against the direction to the
target. For this we can employ the inertia shaping technique [8]
In inertia shaping we control the centroidal composite rigid
body (CRB) inertia [15] or the lockedinertia of the robot.
Cenroidal CRB inertia is the instantaneous rotational inertia
of the robot if all its joints are locked. Unlike linear inertia
the CRB inertia is a function of the robot configuration and
varies continuously.
Approximating the robot as a reaction mass pendulum,
RMP[8], or an inverted pendulum with inertial mass, and
assuming no slip at the ground, its CoM velocity can be
computed as (see Fig. 8):
VG= ωP
G× PG
(11)
where G and ωP
of the pendulum. For best results, we want VG= −c PT for
some scalar c. This can be achieved by setting the desired
angular velocity ωdas follows
Gare the CoM location and the angular velocity
ωd= −ez×PT,
(12)
where ez×PTis a unit vector along the cross product between
by z and PT. The desired locked inertia is obtained as Id=
RIR−1, where I is the current locked inertia and R is the
rotation matrix obtained with an exponential map[9] from ωd.
To implement inertia shaping we string out the 6 unique
elements of the CRB inertia matrix in the form of a vector:
I(3×3)→s?I(6×1). Next we obtain the CRB inertia Jacobian
sponding changes ins?I, i.e.,
δs?I = JIδθ.
JIwhich maps changes in the robot joint angles into corre
(13)
P
T
G
z
VG
Q
D
P
T
G
z
VG
Q
D
Fig. 8.
CoM should be away from T. For this the robot should overall rotate around
an axis obtained by the cross product of PT and the vertical, where P is
the CoP.
To avoid falling on the block, VG, the linear velocity of the robot
To attain Id, the desired joint velocities are:
˙θ = J#
I(Id− I)
(14)
where J#
The humanoid can recruit all the joints to attain Id. The
effect of inertia shaping might not always be big enough to
obtain the desired VG, however, even a modest change is very
useful.
Iis the pseudoinverse of JI.
V. SIMULATION RESULTS
We have performed the simulations using Webots [1], a
commercial mobile robot simulation software developed by
Cyberbotics Ltd. The humanoid fall is simulated with an sharp
push of small duration. We have tested two initial conditions
of the humanoid: standing and walking.
A. Standing humanoid
The humanoid stands on both feet, and is subjected to a
push on its trunk for 0.1s. The push has a magnitude of 200N
forward and 50N to the right. The target for the humanoid
to avoid is located 1.2m in front of it, and the head of the
humanoid would hit it without control. The fall controller starts
0.05s after the push has ended. Inertia shaping, if used, begins
to work as soon as the swinging foot contacts the ground. We
present results with support base change only and with both
of inertia shaping and support base change.
Fig. 9 shows snapshots of the simulation. The support base
changes from a rectangle to a point, then to a quadrilateral
and back to a rectangle. The direction of fall changes, as
expected, according to support base geometry change. When
the humanoid is on double support, it topples forward and
rotates about the front edge of the support base for which the
CoP is located roughly in the middle. Once the robot lifts
the right leg to take a step, it starts toppling around the right
top corner of the left foot and the support base shrinks to a
point (9(b)). Taking a step makes the support base polygon a
quadrilateral (9(c)), and the direction of fall goes to the right
since the reference point is at the right of the support polygon.
Finally the humanoid achieves the rightward fall direction.
Fig. 10 shows the motion of a falling humanoid with
both the support base geometry controller and inertia shaping
controller. After taking a step (10(b)), the humanoid appears
to change the falling direction by rolling the upper body
backward (10(c)).
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−0.2
0
0.2
0.4
0.6
0.8
1
1.2
−0.8−0.6−0.4−0.20 0.2 0.40.60.8
t=0.004
X (m)
Y (m)
convex hull of ASIMO
(a)
−0.2
0
0.2
0.4
0.6
0.8
1
1.2
−0.8−0.6−0.4−0.20 0.2 0.4 0.60.8
t=0.804
X (m)
Y (m)
convex hull of ASIMO
(b)
−0.2
0
0.2
0.4
0.6
0.8
1
1.2
−0.8−0.6−0.4−0.20 0.20.40.60.8
t=1.04
X (m)
Y (m)
convex hull of ASIMO
(c)
−0.2
0
0.2
0.4
0.6
0.8
1
1.2
−0.8−0.6−0.4−0.200.20.40.60.8
t=1.484
X (m)
Y (m)
convex hull of ASIMO
(d)
Fig. 9.
points. The yellow region is the support polygon. The green square is the target object (to avoid). The small red square is CoP, and the cross mark is the
reference point. These two points are connected by the blue line to show the estimated direction of fall. (a) The humanoid gets a forward push. Direction of
the push is shown as the red line. The support polygon is a rectangle formed by the two feet. (b) The humanoid has lifted the right foot to take a step. The
support polygon is simply a point, and it is coincident with the CoP. The reference point implies that the falling direction is toward left. (c) The humanoid
has taken a step. The support polygon is a quadrilateral formed by three points of the right foot and one point of the left foot. (d) The humanoid is falling
leftwards, rotating about the rightmost edge of the right foot.
Upper pictures are the top views of falling humanoid with changing support polygon. The lower figures show the support polygon and significant
Fig. 10.
Fig. 9. Inertia shaping starts after taking a step in the third picture. The humanoid appears to lean its body backwards as if it does limbo. After inertia shaping,
the humanoid has fallen almost backwards.
Snapshots of falling humanoid which uses both the support base geometry controller and inertia shaping controller. The push is the same as in
Comparison of the CoM trajectories for the cases  no
control, support polygon change, and support polygon change
plus inertia shaping  are shown in Fig. 11. The figure clearly
shows that the trajectory of the full controller diverges from the
trajectory of the support polygon change and goes backwards.
0 0.20.40.60.81 1.2
0
0.2
0.4
0.6
0.8
1
1.2
Y of CoM (m)
X of CoM (m)
No control
Geometry control
Geometry control + Inertia shaping
0.40.60.811.2 1.41.61.8
0
20
40
60
80
100
120
140
160
180
time (sec)
angle from the target (deg)
No control
Geometry control
Geometry control + Inertia shaping
Estimated fall angle
Fig. 11.
(Right) of a falling humanoid which was pushes during upright standing. The
humanoid falls on the target without any control, which corresponds to a
0◦avoidance angle. Intelligence footstep control improves this to 100◦and
inertia shaping further improves to 180◦.
Simulation plots of CoM trajectories (Left) and avoidance angles
B. Walking Humanoid
The humanoid is subjected to a forward push during forward
walking. The push is 150N for 0.1 second. Snapshots of the
simulation are shown in Fig. 12. After the push, the humanoid
takes a step on the front right of the ground by the swinging
right leg, and the direction of fall changes to left forward.
Inertia shaping yields a larger angle of falling direction from
the target as shown in Fig. 13.
VI. DISCUSSION AND CONCLUSIONS
We have presented a humanoid robot fall controller, the
objective of which is to rapidly modify the fall direction in
order to avoid hitting an object or a person in the vicinity. The
approach taken by us is to modify the support base geometry
of the robot which indirectly, but strongly, modifies the fall
direction of the robot. The shape of the support base should
be carefully adjusted such that it does not possess any leading
edge facing the object to avoid.
We have implemented the controller through an intelligent
foot placement strategy of the robot, which triggers as soon
as a fall is detected. We have shown how an optimal stepping
786
Page 7
Fig. 12.
change the direction of fall. After the step, it does inertia shaping and tries to move away from the target. Inertia shaping is not efficient enough to change
the direction completely backwards, however the direction deviates a little more compared to a case of changing the support polygon only.
Humanoid gets a forward push while walking. It was supposed to take a step forward, however it has changed the plan and steps rightforward to
−1.2−1−0.8 −0.6−0.4
Y of CoM (m)
−0.20 0.2
0
0.2
0.4
0.6
0.8
1
1.2
X of CoM (m)
No control
Geometry control
Geometry control + Inertia shaping
2.533.54
0
20
40
60
80
100
120
140
160
180
time (sec)
angle from the target (deg)
No control
Geometry control
Geometry control + Inertia shaping
Estimated fall angle
Fig. 13.
(Right) of a falling humanoid which was pushed while walking. Intelligence
footstep control results in a 50◦avoidance angle and inertia shaping further
improves to 120◦.
Simulation plots of CoM trajectories (Left) and avoidance angles
location can be computed, one that maximally diverts the
avoidance angle, the angle between the fall direction and the
direction to avoid. Additionally, we have applied inertia shap
ing controller to further divert the robot. We have demonstrated
our results through the simulation of an Asimolike humanoid
robot.
To our knowledge, this is the first implementation of a
controller that attempts to change the fall direction of a
humanoid robot.
Falling is an unstable motion in nature, and it is hard
to tightly control it. Estimation errors can accumulate in
our method especially because we have used approximate
inverted pendulum models for predicting the robot states at
a future time. Currently, our controller has two distinct phases
including the modification of the support base polygon and
inertia shaping. However, they can be blended together in a
control scheme.
ACKNOWLEDGMENTS
Seungkook was supported by a Honda Research Institute
USA summer internship. Important help from Dr. Kankang
Yin as a summer intern is also gratefully acknowledged.
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