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One of the challenges in humanoid robotics is
motion control. Interacting with humans requires impedance
control algorithms, as well as tackling the problem of the closed
kinematic chains which occur when both feet touch the ground.
However, pure impedance control for totally autonomous robots
is difficult to realize, as this algorithm needs very precise sensors
for force and speed of the actuated parts, as well as very high
sampling rates for the controller input signals. Both requirements
lead to a complex and heavy weight design, which makes up for
heavy machines unusable in RoboCup Soccer competitions.
A lightweight motor controller was developed that can be
used for admittance and impedance control as well as for model
predictive control algorithms to further improve the gait of the
robot.
For the RoboCup Soccer AdultSize League the
humanoid robot Sweaty uses a single fully convolutional neural
network to detect and localize the ball, opponents and other
features on the field of play. This neural network can be trained
from scratch in a few hours and is able to perform in real-time
within the constraints of computational resources available on the
robot. The time it takes to precess an image is approximately
11 ms. Balls and goal posts are recalled in 99 % of all cases
(94.5 % for all objects) accompanied by a false detection rate
of 1.2 % (5.2 % for all). The object detection and localization
helped Sweaty to become finalist for the RoboCup 2017 in
Nagoya.