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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.
The humanoid Sweaty was the finalist in this year’s robocup soccer championship(adult size). For the optimization of the gait and the stability, data concerning forces and torques in the ankle joints would be helpful. In the following paper the development of a six-axis force and torque sensor for the humanoid robot Sweaty is described. Since commercial sensors do not meet the demands for the sensors in Sweatys ankle joints, a new sensor was developed. As a measuring devices we used strain gauges and custom electronics based on an acam PS09. The geometry was analyzed with the FEM program ANSYS to get optimal dimensions for the measuring beams. In addition ANSYS was used to optimize the position for the strain gauges on the beam.
Autonomous humanoid robots require light weight, high torque and high speed actuators to be able to walk and run. For conventional gears with a fixed gear ratio the product of torque and velocity is constant. On the other hand desired motions require maximum torque and speed. In this paper it is shown that with a variable gear ratio it is possible to vary the relation between torque and velocity. This is achieved by introducing systems of rods and levers to move the joints of our humanoid robot ”Sweaty II”. On the basis of a variable gear ratio low speed and high torque can be achieved for those joint angles, which require this motion mode, whereas high speed and low torque can be realized for those joint angles, where it is favorable for the desired motion.