8,909 research outputs found
ZMP support areas for multi-contact mobility under frictional constraints
We propose a method for checking and enforcing multi-contact stability based
on the Zero-tilting Moment Point (ZMP). The key to our development is the
generalization of ZMP support areas to take into account (a) frictional
constraints and (b) multiple non-coplanar contacts. We introduce and
investigate two kinds of ZMP support areas. First, we characterize and provide
a fast geometric construction for the support area generated by valid contact
forces, with no other constraint on the robot motion. We call this set the full
support area. Next, we consider the control of humanoid robots using the Linear
Pendulum Mode (LPM). We observe that the constraints stemming from the LPM
induce a shrinking of the support area, even for walking on horizontal floors.
We propose an algorithm to compute the new area, which we call pendular support
area. We show that, in the LPM, having the ZMP in the pendular support area is
a necessary and sufficient condition for contact stability. Based on these
developments, we implement a whole-body controller and generate feasible
multi-contact motions where an HRP-4 humanoid locomotes in challenging
multi-contact scenarios.Comment: 14 pages, 10 figure
Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs
Traversing environments with arbitrary obstacles poses significant challenges
for bipedal robots. In some cases, whole body motions may be necessary to
maneuver around an obstacle, but most existing footstep planners can only
select from a discrete set of predetermined footstep actions; they are unable
to utilize the continuum of whole body motion that is truly available to the
robot platform. Existing motion planners that can utilize whole body motion
tend to struggle with the complexity of large-scale problems. We introduce a
planning method, called the "Randomized Possibility Graph", which uses
high-level approximations of constraint manifolds to rapidly explore the
"possibility" of actions, thereby allowing lower-level motion planners to be
utilized more efficiently. We demonstrate simulations of the method working in
a variety of semi-unstructured environments. In this context,
"semi-unstructured" means the walkable terrain is flat and even, but there are
arbitrary 3D obstacles throughout the environment which may need to be stepped
over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation
201
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
Humanoid Theory Grounding
In this paper we consider the importance of using a humanoid physical form for a certain proposed kind of robotics, that of theory grounding. Theory grounding involves grounding the theory skills and knowledge of an embodied artificially intelligent (AI) system by developing theory skills and knowledge from the bottom up. Theory grounding can potentially occur in a variety of domains, and the particular domain considered here is that of language. Language is taken to be another “problem space” in which a system can explore and discover solutions. We argue that because theory grounding necessitates robots experiencing domain information, certain behavioral-form aspects, such as abilities to socially smile, point, follow gaze, and generate manual gestures, are necessary for robots grounding a humanoid theory of language
Multi-contact Walking Pattern Generation based on Model Preview Control of 3D COM Accelerations
We present a multi-contact walking pattern generator based on preview-control
of the 3D acceleration of the center of mass (COM). A key point in the design
of our algorithm is the calculation of contact-stability constraints. Thanks to
a mathematical observation on the algebraic nature of the frictional wrench
cone, we show that the 3D volume of feasible COM accelerations is a always a
downward-pointing cone. We reduce its computation to a convex hull of (dual) 2D
points, for which optimal O(n log n) algorithms are readily available. This
reformulation brings a significant speedup compared to previous methods, which
allows us to compute time-varying contact-stability criteria fast enough for
the control loop. Next, we propose a conservative trajectory-wide
contact-stability criterion, which can be derived from COM-acceleration volumes
at marginal cost and directly applied in a model-predictive controller. We
finally implement this pipeline and exemplify it with the HRP-4 humanoid model
in multi-contact dynamically walking scenarios
Beyond Basins of Attraction: Quantifying Robustness of Natural Dynamics
Properly designing a system to exhibit favorable natural dynamics can greatly
simplify designing or learning the control policy. However, it is still unclear
what constitutes favorable natural dynamics and how to quantify its effect.
Most studies of simple walking and running models have focused on the basins of
attraction of passive limit-cycles and the notion of self-stability. We instead
emphasize the importance of stepping beyond basins of attraction. We show an
approach based on viability theory to quantify robust sets in state-action
space. These sets are valid for the family of all robust control policies,
which allows us to quantify the robustness inherent to the natural dynamics
before designing the control policy or specifying a control objective. We
illustrate our formulation using spring-mass models, simple low dimensional
models of running systems. We then show an example application by optimizing
robustness of a simulated planar monoped, using a gradient-free optimization
scheme. Both case studies result in a nonlinear effective stiffness providing
more robustness.Comment: 15 pages. This work has been accepted to IEEE Transactions on
Robotics (2019
Material Recognition CNNs and Hierarchical Planning for Biped Robot Locomotion on Slippery Terrain
In this paper we tackle the problem of visually predicting surface friction
for environments with diverse surfaces, and integrating this knowledge into
biped robot locomotion planning. The problem is essential for autonomous robot
locomotion since diverse surfaces with varying friction abound in the real
world, from wood to ceramic tiles, grass or ice, which may cause difficulties
or huge energy costs for robot locomotion if not considered. We propose to
estimate friction and its uncertainty from visual estimation of material
classes using convolutional neural networks, together with probability
distribution functions of friction associated with each material. We then
robustly integrate the friction predictions into a hierarchical (footstep and
full-body) planning method using chance constraints, and optimize the same
trajectory costs at both levels of the planning method for consistency. Our
solution achieves fully autonomous perception and locomotion on slippery
terrain, which considers not only friction and its uncertainty, but also
collision, stability and trajectory cost. We show promising friction prediction
results in real pictures of outdoor scenarios, and planning experiments on a
real robot facing surfaces with different friction
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