3 research outputs found
Online Balanced Motion Generation for Humanoid Robots
Reducing the complexity of higher order problems can enable solving them in
analytical ways. In this paper, we propose an analytic whole body motion
generator for humanoid robots. Our approach targets inexpensive platforms that
possess position controlled joints and have limited feedback capabilities. By
analysing the mass distribution in a humanoid-like body, we find relations
between limb movement and their respective CoM positions. A full pose of a
humanoid robot is then described with five point-masses, with one attached to
the trunk and the remaining four assigned to each limb. The weighted sum of
these masses in combination with a contact point form an inverted pendulum. We
then generate statically stable poses by specifying a desired upright pendulum
orientation, and any desired trunk orientation. Limb and trunk placement
strategies are utilised to meet the reference CoM position. A set of these
poses is interpolated to achieve stable whole body motions. The approach is
evaluated by performing several motions with an igus Humanoid Open Platform
robot. We demonstrate the extendability of the approach by applying basic
feedback mechanisms for disturbance rejection and tracking error minimisation.Comment: International Conference on Humanoid Robots (Humanoids), Beijing,
China, 201
RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities
Individual and team capabilities are challenged every year by rule changes
and the increasing performance of the soccer teams at RoboCup Humanoid League.
For RoboCup 2019 in the AdultSize class, the number of players (2 vs. 2 games)
and the field dimensions were increased, which demanded for team coordination
and robust visual perception and localization modules. In this paper, we
present the latest developments that lead team NimbRo to win the soccer
tournament, drop-in games, technical challenges and the Best Humanoid Award of
the RoboCup Humanoid League 2019 in Sydney. These developments include a deep
learning vision system, in-walk kicks, step-based push-recovery, and team play
strategies
Fast Whole-Body Motion Control of Humanoid Robots with Inertia Constraints
We introduce a new, analytical method for generating whole-body motions for
humanoid robots, which approximate the desired Composite Rigid Body (CRB)
inertia. Our approach uses a reduced five mass model, where four of the masses
are attributed to the limbs and one is used for the trunk. This compact
formulation allows for finding an analytical solution that combines the
kinematics with mass distribution and inertial properties of a humanoid robot.
The positioning of the masses in Cartesian space is then directly used to
obtain joint angles with relations based on simple geometry. Motions are
achieved through the time evolution of poses generated through the desired foot
positioning and CRB inertia properties. As a result, we achieve short
computation times in the order of tens of microseconds. This makes the method
suited for applications with limited computation resources, or leaving them to
be spent on higher-layer tasks such as model predictive control. The approach
is evaluated by performing a dynamic kicking motion with an igus Humanoid Open
Platform robot.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Paris, France, May 202