Self-localization based on Image Features of Omni-directional Image

Omni-vision system using an omni-mirror is popular to acquire environment information around an autonomous mobile robot. In RoboCup soccer middle size robot league in particular, self-localization methods based on white line extraction on the soccer field are popular. We have studied a self-localization method based on image features, for example, SIFT and SURF, so far. Comparative studies with a conventional self-localization method based on white line extraction are conducted. Compared to the self-localization method based on white line extraction, the method based on image feature can be applied to a general environment with a compact database

Localization of a mobile autonomous robot based on image analysis

This paper introduces an innovative method to solve the problem of self localization of a mobile autonomous robot, and in particular a case study is carried out for robot localization in a RoboCup field environment. The approach here described is completely different from other methods currently used in RoboCup, since it is only based on the use of images and does not involve the use of techniques like Monte Carlo or other probabilistic approaches. This method is simple, acceptably efficient for the purpose it was created, and uses a relatively low computational time to calculate.Fundação para a Ciência e Tecnologia (FCT) - projecto POSI/ROBO/43892/200

Localization of a mobile autonomous robot based on image analysis

This paper introduces an innovative method to solve the problem of self localization of a mobile autonomous robot, and in particular a case study is carried out for robot localization in a RoboCup field environment. The approach here described is completely different from other methods currently used in RoboCup, since it is only based on the use of images and does not involve the use of techniques like Monte Carlo or other probabilistic approaches. This method is simple, acceptably efficient for the purpose it was created, and uses a relatively low computational time to calculate.Fundação para a Ciência e a Tecnologia (FCT) - POSI/ROBO/43892/200

IMPLEMENTATION OF A LOCALIZATION-ORIENTED HRI FOR WALKING ROBOTS IN THE ROBOCUP ENVIRONMENT

This paper presents the design and implementation of a human–robot interface capable of evaluating robot localization performance and maintaining full control of robot behaviors in the RoboCup domain. The system consists of legged robots, behavior modules, an overhead visual tracking system, and a graphic user interface. A human–robot communication framework is designed for executing cooperative and competitive processing tasks between users and robots by using object oriented and modularized software architecture, operability, and functionality. Some experimental results are presented to show the performance of the proposed system based on simulated and real-time information. </jats:p

Artificial Intelligence and Systems Theory: Applied to Cooperative Robots

This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to shoot a ball towards the goal, but also to detect and avoid static (walls, stopped robots) and dynamic (moving robots) obstacles. Furthermore, they must cooperate to defeat an opposing team. Our past and current research in soccer robotics includes cooperative sensor fusion for world modeling, object recognition and tracking, robot navigation, multi-robot distributed task planning and coordination, including cooperative reinforcement learning in cooperative and adversarial environments, and behavior-based architectures for real time task execution of cooperating robot teams