168 research outputs found
Inverse, forward and other dynamic computations computationally optimized with sparse matrix factorizations
We propose an algorithm to compute the dynamics of articulated rigid-bodies
with different sensor distributions. Prior to the on-line computations, the
proposed algorithm performs an off-line optimisation step to simplify the
computational complexity of the underlying solution. This optimisation step
consists in formulating the dynamic computations as a system of linear
equations. The computational complexity of computing the associated solution is
reduced by performing a permuted LU-factorisation with off-line optimised
permutations. We apply our algorithm to solve classical dynamic problems:
inverse and forward dynamics. The computational complexity of the proposed
solution is compared to `gold standard' algorithms: recursive Newton-Euler and
articulated body algorithm. It is shown that our algorithm reduces the number
of floating point operations with respect to previous approaches. We also
evaluate the numerical complexity of our algorithm by performing tests on
dynamic computations for which no gold standard is available.Comment: 8 pages, 2 figure, conference RCAR 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
On-line Joint Limit Avoidance for Torque Controlled Robots by Joint Space Parametrization
This paper proposes control laws ensuring the stabilization of a time-varying
desired joint trajectory, as well as joint limit avoidance, in the case of
fully-actuated manipulators. The key idea is to perform a parametrization of
the feasible joint space in terms of exogenous states. It follows that the
control of these states allows for joint limit avoidance. One of the main
outcomes of this paper is that position terms in control laws are replaced by
parametrized terms, where joint limits must be avoided. Stability and
convergence of time-varying reference trajectories obtained with the proposed
method are demonstrated to be in the sense of Lyapunov. The introduced control
laws are verified by carrying out experiments on two degrees-of-freedom of the
humanoid robot iCub.Comment: 8 pages, 4 figures. Submitted to the 2016 IEEE-RAS International
Conference on Humanoid Robot
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
Automatic Gain Tuning of a Momentum Based Balancing Controller for Humanoid Robots
This paper proposes a technique for automatic gain tuning of a momentum based
balancing controller for humanoid robots. The controller ensures the
stabilization of the centroidal dynamics and the associated zero dynamics.
Then, the closed-loop, constrained joint space dynamics is linearized and the
controller's gains are chosen so as to obtain desired properties of the
linearized system. Symmetry and positive definiteness constraints of gain
matrices are enforced by proposing a tracker for symmetric positive definite
matrices. Simulation results are carried out on the humanoid robot iCub.Comment: Accepted at IEEE-RAS International Conference on Humanoid Robots
(HUMANOIDS). 201
On Centroidal Dynamics and Integrability of Average Angular Velocity
In the literature on robotics and multibody dynamics, the concept of average
angular velocity has received considerable attention in recent years. We
address the question of whether the average angular velocity defines an
orientation framethat depends only on the current robot configuration and
provide a simple algebraic condition to check whether this holds. In the
language of geometric mechanics, this condition corresponds to requiring the
flatness of the mechanical connection associated to the robotic system. Here,
however, we provide both a reinterpretation and a proof of this result
accessible to readers with a background in rigid body kinematics and multibody
dynamics but not necessarily acquainted with differential geometry, still
providing precise links to the geometric mechanics literature. This should help
spreading the algebraic condition beyond the scope of geometric
mechanics,contributing to a proper utilization and understanding of the concept
of average angular velocity.Comment: 8 pages, accepted for IEEE Robotics and Automation Letters (RA-L
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