ambarish.com Ambarish Goswami's Current Research |
---|
Home/ Current Research/ Safe fall strategies for humanoid robots |
|
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.
One can ignore the possibility of a fall and wishfully hope that its effects will not be serious. However, failure studies, such as in car crash, have taught us against behaving according to this instinct. In fact, planning and simulation of failure situations can have enormous benefits, including system design improvements, and support for user safety and confidence. Following this philosophy we closely focus our attention to the phenomenon of humanoid fall and attempt to develop a comprehensive control strategy to deal with this undesired and traumatic ``failure'' event.
Humanoid robot falls among multiple objects. With no control, the robot nmay hit an object.
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. We currently 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 independently 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. Here we introduce a strategy for fall direction change and describe a controller which can achieve both objectives. Let us note that a fall controller is not a balance controller. A fall controller complements, and does not replace, a balance 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 push-recovery controller. A push-recovery 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. We propose a fall strategy which rapidly modifies the fall direction of a robot in order to avoid hitting a person or an object in the vicinity. Our approach is based on the optimal modification of the support base geometry of the robot through intelligent stepping. Additional improvement to the fall controller is achieved through inertia shaping technique aimed at controlling the centroidal rotational inertia of the robot. The video is composed of two simulation animations which show the falling motion of an Asimo-like humanoid robot. In the first simulation only the footstep controller is active. In the second animation, the inertia shaping controller is activated as soon as the humanoid makes a foot touchdown.
|
Abstract:
This paper addresses a new control strategy to
reduce the damage to a humanoid robot during a fall. Instead
of following the traditional approach of finding a favorable
configuration with which to fall to the ground, this method
attempts to stop the robot from falling all the way to the ground.
This prevents the full transfer of the robot’s potential energy
to kinetic energy, and consequently results in a milder impact.
The controlled motion of the falling robot involves a sequence of
three deliberate contacts to the ground with the swing foot and
two hands, in that order. In the final configuration the robot’s
center of mass (CoM) remains relatively high from the floor
and the robot has a relatively stable three-point contact with
the ground; hence the name tripod fall. The optimal location of
the three contacts are learned through reinforcement learning
algorithm. The controller is simulated on a full size humanoid,
and experimentally tested on the NAO humanoid robot. In this
work we apply our fall controller only to a forward fall.
Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):
With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:
With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:
Simulation video:
Click Here
Abstract:
Humanoid robots are expected to share human
environments in the future and it is important to ensure the
safety of their operation. A serious threat to safety is the
fall of such robots, which can seriously damage the robot
itself as well as objects in its surrounding. Although fall is a
rare event in the life of a humanoid robot, the robot must be
equipped with a robust fall strategy since the consequences
of fall can be catastrophic. In this paper we present a strategy
to change the default fall direction of a robot, during the fall.
By changing the fall direction the robot may avoid falling
on a delicate object or on a person. Our approach is based
on the key observation that the toppling motion of a robot
necessarily occurs at an edge of its support area. To modify
the fall direction the robot needs to change the position and
orientation of this edge vis-a-vis the prohibited directions.
We achieve this through intelligent stepping as soon as the
fall is predicted. We compute the optimal stepping location
which results in the safest fall. Additional improvement to the
fall controller is achieved through inertia shaping, which is
a principled approach aimed at manipulating the robot’s cen-
troidal inertia, thereby indirectly controlling its fall direction.
We describe the theory behind this approach and demonstrate
our results through simulation and experiments of the Alde-
baran NAO H25 robot. To our knowledge, this is the first
implementation of a controller that attempts to change the
fall direction of a humanoid robot.
Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):
With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:
With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:
Simulation video:
Click Here
Abstract:
Although the fall of a humanoid robot is rare in controlled environments,
it cannot be avoided in the real world where the robot may physically
interact with the environment.
Our earlier work introduced the strategy of direction-changing fall,
in which the robot attempts to reduce the chance of human injury
by changing its default fall direction in real-time and falling in a safer direction.
The current paper reports further theoretical developments culminating in a successful hardware
implementation of this fall strategy conducted on the Aldebaran NAO robot.
This includes new algorithms for humanoid kinematics and
Jacobians involving coupled joints and a complete estimation of the body frame
attitude using an additional inertial measurement unit.
Simulations and experiments are smoothly handled by
our platform independent humanoid control software package called Locomote.
We report experiment scenarios where we demonstrate the effectiveness of the proposed strategies
in changing humanoid fall direction.
Without a fall controller, when a humanoid is pushed from behind (left photo), it topples forward (middle photo) and falls on its face (right photo):
With a fall control strategy, which in this case is lifting right foot, the humanoid can fall to the right under the same push force as above:
With an inertia shaping fall controller, the humanoid can fall diagonally, again under the same push force as above:
Simulation video:
Click Here
Simulation videos:
Without fall control, the robot would fall in a messy way on its arms: Click Here
With fall control, the robot falls on its backpack :
Click Here
Back to Current Research |
Back to Main Page |