1,332 research outputs found
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015
Momentum Control of Humanoid Robots with Series Elastic Actuators
Humanoid robots may require a degree of compliance at the joint level for
improving efficiency, shock tolerance, and safe interaction with humans. The
presence of joint elasticity, however, complexifies the design of balancing and
walking controllers. This paper proposes a control framework for extending
momentum based controllers developed for stiff actuators to the case of series
elastic actuators. The key point is to consider the motor velocities as an
intermediate control input, and then apply high-gain control to stabilise the
desired motor velocities achieving momentum control. Simulations carried out on
a model of the robot iCub verify the soundness of the proposed approach
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Torque-Controlled Stepping-Strategy Push Recovery: Design and Implementation on the iCub Humanoid Robot
One of the challenges for the robotics community is to deploy robots which
can reliably operate in real world scenarios together with humans. A crucial
requirement for legged robots is the capability to properly balance on their
feet, rejecting external disturbances. iCub is a state-of-the-art humanoid
robot which has only recently started to balance on its feet. While the current
balancing controller has proved successful in various scenarios, it still
misses the capability to properly react to strong pushes by taking steps. This
paper goes in this direction. It proposes and implements a control strategy
based on the Capture Point concept [1]. Instead of relying on position control,
like most of Capture Point related approaches, the proposed strategy generates
references for the momentum-based torque controller already implemented on the
iCub, thus extending its capabilities to react to external disturbances, while
retaining the advantages of torque control when interacting with the
environment. Experiments in the Gazebo simulator and on the iCub humanoid robot
validate the proposed strategy
Distributed Bio-inspired Humanoid Posture Control
This paper presents an innovative distributed bio-inspired posture control
strategy for a humanoid, employing a balance control system DEC (Disturbance
Estimation and Compensation). Its inherently modular structure could
potentially lead to conflicts among modules, as already shown in literature. A
distributed control strategy is presented here, whose underlying idea is to let
only one module at a time perform balancing, whilst the other joints are
controlled to be at a fixed position. Modules agree, in a distributed fashion,
on which module to enable, by iterating a max-consensus protocol. Simulations
performed with a triple inverted pendulum model show that this approach limits
the conflicts among modules while achieving the desired posture and allows for
saving energy while performing the task. This comes at the cost of a higher
rise time.Comment: 2019 41st Annual International Conference of the IEEE Engineering in
Medicine & Biology Society (EMBC
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