Catálogo de publicaciones - libros

The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments such as RoboCup competitions since other agent behaviors may cause sudden changes of state transition probabilities of which constancy is necessary for the learning to converge. A modular learning approach would be able to solve this problem if a learning agent can assign each module to one situation in which the module can regard the state transition probabilities as constant. This paper presents a method of modular learning in a multiagent environment, by which the learning agent can adapt its behaviors to the situations as results of the other agent’s behaviors. Scheduling for learning is introduced to avoid the complexity in autonomous situation assignment.

An object recognition process in general is designed as a domain specific, highly specialized task. As the complexity of such a process tends to be rather inestimable, machine learning is used to achieve better results in recognition. The goal of the process presented in this paper is the computation of the pose of a visible robot, i. e. the distance, angle, and orientation. The recognition process itself, the division into subtasks, as well as the results of the process are presented. The algorithms involved have been implemented and tested on a Sony Aibo.

We show that previously published visual data processing methods for the simulated robotic soccer so far have not been utilizing all available information, because they were mainly based on heuristic considerations. Researchers have approached to estimating the agent location and orientation as two separate tasks, which caused systematic errors in the angular measurements. Further attempts to get rid of them (e.g. by completely neglecting the angular data) only aggravated the problem and resulted in the losses in the accuracy. We utilize all the potential of the visual sensor by jointly estimating the agent view direction angle and Cartesian coordinates using the extended Kalman filtering technique. Our experiments showed that the achievable average error limit for this particular application is about 25-33 per cent lower than that of the best algorithms published by far.

Path planning technique is proposed in the paper. It was developed for robots with differential drive, but with minor modification could be used for all types of nonholonomic robots. The path was planned in the way to minimize the time of reaching end point in desired direction and with desired velocity, starting from the initial state described by the start point, initial direction and initial velocity. The constraint was acceleration limit in tangential and radial direction caused by the limited grip of the tires. The path is presented as the spline curve and was optimised by placing the control points trough which the curve should take place.

Fast and accurate self-localization is one of the most important problems in autonomous mobile robots. In this paper, an analysis by synthesis method is presented for optimizing the self-localization procedure. In the synthesis phase of this method, the robot’s observation of the field is predicted using the results of odometry. It is done by calculating the position of the landmarks on the captured image. In the analysis phase, the local search algorithms find the exact position of the landmarks on the image from which the best matching coordinates of the robot are determined using a likelihood function. The final coordinates of the robot are then obtained from the odometry sensor, using an integrated delay compensation and correction technique. Experimental results show that precise and delay-free results are achieved with a very low computational cost.

In this study, a new method called Reverse Monte Carlo Localization (R-MCL) for global localization of autonomous mobile agents in the robotic soccer domain is proposed to overcome the uncertainty in the sensors, environment and the motion model. This is a hybrid method based on both Markov Localization(ML) and Monte Carlo Localization(MCL) where the ML module finds the region where the robot should be and MCL predicts the geometrical location with high precision by selecting samples in this region. The method is very robust and fast and requires less computational power and memory compared to similar approaches and is accurate enough for high level decision making which is vital for robot soccer.

Soccer simulation commentary system is a suitable test bed for exploring . The simulation environment requires that the system generates real time comments based on the information received from the . In this article, a three-layer architecture of Caspian Soccer Commentary system is presented, and each component of the system is briefly described. The emphasis of this paper is on design and implementation of the and the subsystems. The Analyzer takes advantage of the to keep track of the game situations. The and mechanism is proposed to improve the efficiency of the Content Selector subsystem. The presented Commentary System together with the other Caspian presentation and analysis tools won the first place in RoboCup 2003 Game Presentation and Match Analysis competitions.

The paper discusses a top-down approach to model soccer knowledge, as it can be found in soccer theory books. The goal is to model soccer strategies and tactics in a way that they are usable for multiple RoboCup soccer leagues, i.e. for different hardware platforms. We investigate if and how soccer theory can be formalized such that specification and execution is possible. The advantage is clear: theory abstracts from hardware and from specific situations in leagues. We introduce basic primitives compliant with the terminology known in soccer theory, discuss an example on an abstract level and formalize it. We then consider aspects of different RoboCup leagues in a case study and examine how examples can be instantiated in three different leagues.

Motion detection and tracking while moving is a desired ability for any soccer player. For instance, this ability allows the determination of the ball trajectory when the player is moving himself or when he is moving his head, for making or planning a soccer-play. If a robot soccer player should have a similar functionality, then it requires an algorithm for real-time movement analysis and tracking that performs well when the camera is moving. The aim of this paper is to propose such an algorithm for an AIBO robot. The proposed algorithm uses motion compensation for having a stabilized background, where the movement is detected, and Kalman Filtering for a robust tracking of the moving objects. The algorithm can be adapted for almost any kind of mobile robot. Results of the motion detection and tracking algorithm, working in real-world video sequences, are shown.

This paper describes methods used in stabilizing the walking gait of , a small humanoid robot given rate feedback from two RC gyroscopes. is a fully autonomous small humanoid robot (30cm tall). Although uses a minimal set of actuators and sensors, it has proven itself in international competitions, winning honors at the RoboCup and HuroSot competitions in 2002 and 2003. The feedback control law is based solely on the rate information from two RC gyroscopes. This alleviates drift problems introduced by integrating the RC gyroscope feedback in the more common position control approaches.