Oliver Tenchio

Reinforcing the Driving Quality of Soccer Playing Robots by Anticipation Verbesserung der Fahreigenschaften von fußballspielenden Robotern durch Antizipation

Summary This paper shows how an omnidirectional robot can learn to correct inaccuracies when driving, or even learn to use corrective motor commands when a motor fails, whether partially or completely. Driving inaccuracies are unavoidable, since not all wheels have the same grip on the surface, or not all motors can provide exactly the same power. When a robot starts driving, the real system response differs from the ideal behavior assumed by the control software. Also, malfunctioning motors are a fact of life that we have to take into account. Our approach is to let the control software learn how the robot reacts to instructions sent from the control computer. We use a neural network, or a linear model for learning the robots response to the commands. The model can be used to predict deviations from the desired path, and take corrective action in advance, thus increasing the driving accuracy of the robot. The model can also be used to monitor the robot and assess if it is performing according to its learned response function. If it is not, the new response function of the malfunctioning robot can be learned and updated. We show, that even if a robot loses power from a motor, the system can re-learn to drive the robot in a straight path, even if the robot is a black-box and we are not aware of how the commands are applied internally.

Learning to drive and simulate autonomous mobile robots

We show how to apply learning methods to two robotics problems, namely the optimization of the on-board controller of an omnidirectional robot, and the derivation of a model of the physical driving behavior for use in a simulator. We show that optimal control parameters for several PID controllers can be learned adaptively by driving an omni directional robot on a field while evaluating its behavior, using an reinforcement learning algorithm. After training, the robots can follow the desired path faster and more elegantly than with manually adjusted parameters. Secondly, we show how to learn the physical behavior of a robot. Our system learns to predict the position of the robots in the future according to their reactions to sent commands. We use the learned behavior in the simulation of the robots instead of adjusting the physical simulation model whenever the mechanics of the robot changes. The updated simulation reflects then the modified physics of the robot.

Parabolic Flight Reconstruction from Multiple Images from a Single Camera in General Position

This paper shows that it is possible to retrieve all parameters of the parabolic flight trajectory of an object from a time stamped sequence of images captured by a single camera looking at the scene. Surprisingly, it is not necessary to use two cameras (stereo vision) in order to determine the coordinates of the moving object with respect to the floor. The technique described in this paper can thus be used to determine the three-dimensional trajectory of a ball kicked by a robot. The whole calculation can be done, at the limit, with just three measurements of the ball position captured in three consecutive frames. Therefore, this technique can be used to forecast the future motion of the ball a few milliseconds after the kick has taken place. The computation is fast and allows a robot goalie to move to the correct blocking position. Interestingly, this technique can also be used to self-calibrate stereo cameras.

A Novel Omnidirectional Wheel Based on Reuleaux-Triangles

This paper discusses the mechanical design and simulation of a novel omnidirectional wheel based on Reuleaux-triangles. The main feature of our omniwheel is that the point of contact of the wheel with the floor is always kept at the same distance from the center of rotation by mechanical means. This produces smooth translational movement on a flat surface, even when the profile of the complete wheel assembly has gaps between the passive rollers. The grip of the wheel with the floor is also improved. The design described in this paper is ideal for hard surfaces, and can be scaled to fit small or large vehicles. This is the first design for an omnidirectional wheel without circular profile, yet capable of rolling smoothly on a hard surface.

The Soul of A New Machine: The Soccer Robot Team of the FU Berlin

This paper describes the hardware and software of the robotic soccer team built at the Freie Universitat Berlin which took part in the 2000 RoboCup Championship in Melbourne, Australia. Our team, the FU Fighters, consists of five robots of less than 18 cm horizontal cross-section. Four of the robots have the same mechanical design, while the goalie is slightly different. All the hardware was designed and assembled at the FU Berlin. The paper describes the hierarchical control architecture used to generate the behavior of individual agents and the whole team. Our reactive approach is based on the dual dynamics framework proposed by Jager, but extended with a third module of sensor readings. Fast changing sensors are aggregated in time to form slowly changing percepts in a temporal resolution hierarchy. We describe the main blocks of the software and their interactions.