Catálogo de publicaciones - libros

RoboCup is an international initiative with the main goals of fostering research and education in Artificial Intelligence and Robotics, as well as of promoting Science and Technology to world citizens. The idea is to provide a standard problem where a wide range of technologies can be integrated and examined, as well as being used for project-oriented education, and to organize annual events open to the general public, where different solutions to the problem are compared.

In this paper we propose an approach for tracking a moving target using Rao-Blackwellised particle filters. Such filters represent posteriors over the target location by a mixture of Kalman filters, where each filter is conditioned on the discrete states of a particle filter. The discrete states represent the non-linear parts of the state estimation problem. In the context of target tracking, these are the non-linear motion of the observing platform and the different motion models for the target. Using this representation, we show how to reason about physical interactions between the observing platform and the tracked object, as well as between the tracked object and the environment. The approach is implemented on a four-legged AIBO robot and tested in the context of ball tracking in the RoboCup domain.

UCHILSIM is a robotic simulator specially developed for the RoboCup four-legged league. It reproduces with high accuracy the dynamics of AIBO motions and its interactions with the objects in the game field. Their graphic representations within the game field also possess a high level of detail. The main design goal of the simulator is to become a platform for learning complex robotic behaviors which can be directly transferred to a real robot environment. UCHILSIM is able to adapt its parameters automatically, by comparing robot controller behaviors in reality and in simulations. So far, the effectiveness of UCHILSIM has been tested in some robot learning experiments which we briefly discuss hereinafter. We believe that the use of a highly realistic simulator might speed up the progress in the four legged league by allowing more people to participate in our challenge.

RoboCup has hosted a coach competition for several years creating a challenging testbed for research in advice-giving agents. A coach agent is expected to advise an unknown coachable team. In RoboCup 2003, the coachable agents could process the coach’s advice but did not include a protocol for communication among them. In this paper we present CommLang, a standard for agent communication which will be used by the coachable agents in the simulation league at RoboCup 2004. The communication standard supports representation of multiple message types which can be flexibly combined in a single utterance. We then describe the application of CommLang in our coachable agents and present empirical results showing the communication’s effect on world model completeness and accuracy. Communication in our agents improved the fraction of time which our agents are confident of player and ball locations and simultaneously improved the overall accuracy of that information.

The Segway Human Transport (HT) is a one person dynamically self-balancing transportation vehicle. The Segway Robot Mobility Platform (RMP) is a modification of the HT capable of being commanded by a computer for autonomous operation. With these platforms, we propose a new domain for human-robot coordination through a competitive game: Segway Soccer. The players include robots (RMPs) and humans (riding HTs). The rules of the game are a combination of soccer and Ultimate Frisbee rules. In this paper, we provide three contributions. First, we describe our proposed Segway Soccer domain. Second, we examine the capabilities and limitations of the Segway and the mechanical systems necessary to create a robot Segway Soccer Player. Third, we provide a detailed analysis of several ball manipulation/kicking systems and the implementation results of the CM-RMP pneumatic ball manipulation system.

We describe a new method for detecting features on a marked RoboCup field. We implemented the framework for robots with omnidirectional vision, but the method can be easily adapted to other systems. The focus is on the recognition of the center circle and four different corners occurring in the penalty area. Our differs from previous methods, in that we aim to detect a whole palette of different features, hierarchically ordered and possibly containing each other. High-level features, such as the center circle or the corners, are constructed from low-level features such as arcs and lines. The feature detection process starts with low-level features and iteratively constructs higher features. In RoboCup the method is valuable for robot self-localization; in other fields of application the method is useful for object recognition using shape information.

We propose to use a robust method for appearance-based matching that has been shown to be insensitive to illumination and occlusion for robot self-localization. The drawback of this method is that it relies on panoramic images taken in one certain orientation, restricts the heading of the robot throughout navigation or needs additional sensors for orientation, e.g. a compass. To avoid these problems we propose a combination of the appearance-based method with odometry data. We demonstrate the robustness of the proposed self-localization against changes in illumination by experimental results obtained in the RoboCup Middle-Size scenario.

In this paper, we present a new approach for omnidirectional vision-based self-localization in the RoboCup Middle-Size League. The omnidirectional vision sensor is used as a range finder (like a laser or a sonar) sensitive to colors transitions instead of nearest obstacles. This makes it possible to have a more reach information about the environment, because it is possible to discriminate between different objects painted in different colors. We implemented a Monte-Carlo localization system slightly adapted to this new type of range sensor. The system runs in real time on a low-cost pc. Experiments demonstrated the robustness of the approach. Event if the system was implemented and tested in the RoboCup Middle-Size field, the system could be used in other environments.

In the RoboCup four-legged league, robots mainly rely on artificial coloured landmarks for localisation. As it was done in other leagues, artificial landmarks will soon be removed as part of the RoboCup push toward playing in more natural environments. Unfortunately, the robots in this league have very unreliable odometry due to poor modeling of legged locomotion and to undetected collisions. This makes the use of robust sensor-based localization a necessity. We present an extension of our previous technique for fuzzy self-localization based on artificial landmarks, by including observations of features that occur naturally in the soccer field. In this paper, we focus on the use of corners between the field lines. We show experimental results obtained using these features together with the two nets. Eventually, our approach should allow us to migrate from landmarks-only to line-only localisation.

This paper describes a behavior-based architecture which integrates existing potential field approaches concerning motion planning as well as the evaluation and selection of actions into a single architecture. This combination allows, together with the concept of competing behaviors, the specification of more complex behaviors than the usual approach which is focusing on behavior superposition and is mostly dependent on additional external mechanisms. The architecture and all methods presented in this paper have been implemented and applied to different robots.