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@MISC{Kim2014-ID904,
author = {Kim, S.},
title = {Rapid and reactive robot control framework for catching objects in flight},
howpublished = {PhD Thesis},
year = {2014},
abstract = {Humans can react extremely rapidly in the face of unexpected changes in
the environment. This is best illustrated in sports, when tennis players
run and return a fast ball flying at a speed of around 73 m=s. Robots, on
the other hand, remain slow and clumsy to adapt to perturbations, despite the
fact that computers process information orders of magnitude faster than the
human brain. This thesis targets the design of controllers to endow robot with
extremely fast and appropriate reactivity in the face of unforeseen changes in
the environment. As a benchmark, we chose the challenging task of catching
objects in flight. To react appropriately to perturbations requires the ability to
detect and predict the effect of the observed changes in the environment and to
adapt its control plan adequately.
This thesis, hence, addresses first the problem of predicting accurately the
flying trajectory of the object. We propose a model-free method to estimate the
dynamics of free-flying objects. We take a realistic perspective to the problem
and investigate tracking accurately and very rapidly the trajectory and orientation
of an object so as to catch it in flight. We consider the dynamics of
complex objects where the grasping point is not located at the center of mass,
without having any prior information on the physical properties of the object.
We also consider the dynamics of non-rigid object (such as a half-filled bottle).
It is challenging as inertial properties of the object are not even constant and
may change during flight. To achieve this, a density estimate of the translational
and rotational acceleration is built based on the trajectories of various
examples by using a machine learning approach. The estimated model of the
objectís dynamics is a closed form solution, and it is used in conjunction with
an Extended Kalman Filter for robust tracking in the face of noisy sensing. We
validate the approach for real-time motion tracking of 5 daily life objects with
complex dynamics (a ball, a fully-filled bottle, a half-filled bottle, a hammer
and a pingpong racket).
In the second part of the thesis, we propose a novel methodology for learning
when and where to grasp the flying object. We develop a data-driven probabilistic
approach to estimate a distribution of admissible grasping postures on
the object and to compute the robotís reachable space. The robot is thus able
to determine the feasible catching arm configuration (mid-flight intercept point)
along the predicted object trajectory in real-time. The method is extended first
to compute feasible bimanual grasping posture, in a second stage, to determine
whole-body catching posture of a 29 degrees of freedom (DOF) humanoid robot
(excluding the fingers and neck). Finally, we show that the models of reachable
space which we developed for our robotic framework can explain observed
preference in posture in human catching motions.
In the last part of the thesis, we address the issue of adapting on the fly
the robotís arm motion so as to catch the flying object on time. We adopt a
dynamical system (DS) approach to control simultaneously and in coordination
the motion of the arm and fingers, so that the fingers close on time on the object.
Additionally, we propose a system to synchronize the robotís motion with that
of the fast moving objects, while benefitting from all the robustness properties
deriving from the DS. Furthermore, we propose a generalized human-like inversekinematics
solution, by modeling human-like characteristics (the degree of torso
orientation and elbow elevation) from human demonstrations and by applying
the model to a generalized inverse kinematic problem. The humanoid robot is
thus able to increase the human-likeness while it executes the trained task-space
motion.
We have validated the methods developed in the thesis, in simulation and
real-world experiment with different robot platforms, iCub (53 DOF) and COMAN
(29 DOF) humanoid robots and KUKA LWR robot arm (7 DOF). In
particular, we demonstrated the extremely fast speed of our method in an impressive
demonstration, whereby the KUKA LWR robot arm catches in-flight
different objects with uneven mass distribution, such as a tennis racket and
a bottle partly filled with water. We believe that our methods significantly
advances the field, in offering an example of ultra-fast control in the face of
uncertainty.},
}


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