Recent Papers:
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S.-K. Yun and A. Goswami,
Momentum-Based Reactive Stepping Controller
on Level and Non-level Ground for Humanoid Robot Push Recovery,
IROS 2011, San Francisco, CA, September 2011.
(pdf).
This paper introduces the General Foot Placement Estimator (GFPE) point. The GFPE point is the point at which
a robot should step to, on level or non-level surface, after a push, in order to come to a complete stop with a
vertically upright configuration.
Abstract:
This paper presents a momentum-based reactive stepping controller
for humanoid robot push recovery.
By properly regulating combinations of linear and angular momenta,
the controller can selectively encourage the robot to recover its balance with or without taking a step.
A reference stepping location is computed by
modeling the humanoid as a passive rimless wheel with two spokes
such that stepping on the location leads to a complete stop of the wheel at the vertically upright position.
In contrast to most reference points for stepping based on pendulum models such as the capture point,
our reference point exists on both level and non-level grounds.
Moreover, in contrast with continuously evolving step locations,
our step location is stationary.
The computation of the location of the reference point also generates
the duration of step which can be used for designing a stepping trajectory.
Momentum-based stepping for push recovery is implemented
in simulations of a full size humanoid on 3D non-level ground.
Humanoid stepping on a 11.5 degree uphill under various push forces:
Humanoid stepping on a 6 degree downhill under various push forces:
Simulation video:
Click Here
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S-H. Lee and A. Goswami,
Fall on Backpack: Damage Minimizing Humanoid Fall on Targeted Body Segment Using Momentum Control,
ASME 2011
8th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) inside International
Design Engineering Technical Conference (IDETC), Washington DC, USA, August 2011.
(pdf).
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 damage-resistant design and apply impact-absorbing 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 re-orient 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.
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
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S. Kalyanakrishnan and A. Goswami,
Learning to Predict Humanoid Fall,
The International Journal of Humanoid Robotics, Vol. 8, No. 2 (2011).
(pdf).
Abstract
Falls are undesirable in humanoid robots, but also inevitable, especially as robots
get deployed in physically interactive human environments. We consider the problem of
fall prediction: to predict if the balance controller of a robot can prevent a fall from the
robot’s current state. A trigger from the fall predictor is used to switch the robot from
a balance maintenance mode to a fall control mode. It is desirable for the fall predictor
to signal imminent falls with sufficient lead time before the actual fall, while minimizing
false alarms. Analytical techniques and intuitive rules fail to satisfy these competing
objectives on a large robot that is subjected to strong disturbances and exhibits complex
dynamics. We contribute a novel approach to engineer fall data such that existing
supervised learning methods can be exploited to achieve reliable prediction. Our method
provides parameters to control the tradeoff between the false positive rate and lead time.
Several combinations of parameters yield solutions that improve both the false positive
rate and the lead time of hand-coded solutions. Learned solutions are decision lists with
typical depths of 5–10, in a 16-dimensional feature space. Experiments are carried out
in simulation on an ASIMO-like robot.
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Gabriel Aguirre-Ollinger, J. Edward Colgate,
Michael A. Peshkin, and A. Goswami,
Design of an Active 1-DOF Lower-Limb Exoskeleton with Inertia Compensation,
The International Journal of Robotics Research, vol. 30, no. 4, April 2011.
(pdf).
My affiliations in the paper are incorrect, please note the Corrigendum
(pdf).
Abstract
Limited research has been done on exoskeletons to enable faster movements of the lower extremities.
An exoskeleton's mechanism can actually hinder agility by adding weight, inertia and friction to the legs;
compensating inertia through control is particularly dicult due to instability issues. The added inertia
will reduce the natural frequency of the legs, probably leading to lower step frequency during walking.
We present a control method that produces an approximate compensation of an exoskeleton's inertia.
The aim is making the natural frequency of the exoskeleton-assisted leg larger than that of the unaided
leg. The method uses admittance control to compensate the weight and friction of the exoskeleton.
Inertia compensation is emulated by adding a feedback loop consisting of low-pass ltered acceleration
multiplied by a negative gain. This gain simulates negative inertia in the low-frequency range. We tested
the controller on a statically supported, single-DOF exoskeleton that assists swing movements of the leg.
Subjects performed movement sequences, rst unassisted and then using the exoskeleton, in the context
of a computer-based task resembling a race. With zero inertia compensation, the steady-state frequency
of leg swing was consistently reduced. Adding inertia compensation enabled subjects to recover their
normal frequency of swing.
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S-H. Lee and A. Goswami,
Ground reaction force control at each foot:
A momentum-based humanoid balance controller for
non-level and non-stationary ground,
IROS 2010, Taipei, Taiwan, October 2010.
(pdf).
Abstract:
We present a novel momentum-based method for maintaining balance of
humanoid robots.
By controlling the desired ground reaction force (GRF) and
center of pressure (CoP) at each support foot, our method can naturally deal with
non-level and non-stationary ground at each foot-ground contact, as well as different
frictional properties. We do not make use of the net GRF and CoP which may
be difficult or impossible to compute for non-level grounds.
Our method minimizes the ankle torques during double support.
We show the effectiveness of this new balance control method
by simulating various experiments with a humanoid robot
including maintaining balance when two feet are on separate moving
supports with different inclinations and velocities.
Here is a simulation video: Click Here
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