Abstract: Autonomous mobile robots can be found in a wide
field of applications. Their types range from household robots over
workshop robots to autonomous cars and many more. All of them
undergo a number of testing steps during development, production
and maintenance. This paper describes an approach to improve
testing of robot behavior. It was inspired by the RoboCup @work
competition that itself reflects a robotics benchmark for industrial
robotics. There, scaled down versions of mobile industrial robots
have to navigate through a workshop-like environment or operation
area and have to perform tasks of manipulating and transporting
work pieces. This paper will introduce an approach of automated
vision-based testing of the behavior of the so called youBot robot,
which is the most widely used robot platform in the RoboCup
@work competition. The proposed system allows automated testing
of multiple tries of the robot to perform a specific missions and
it allows for the flexibility of the robot, e.g. selecting different
paths between two tasks within a mission. The approach is based
on a multi-camera setup using, off the shelf cameras and optical
markers. It has been applied for test-driven development (TDD) and
maintenance-like verification of the robot behavior and performance.
Abstract: This paper presents an optimized algorithm for robot localization which increases the correctness and accuracy of the estimating position of mobile robot to more than 150% of the past methods [1] in the uncertain and noisy environment. In this method the odometry and vision sensors are combined by an adapted well-known discrete kalman filter [2]. This technique also decreased the computation process of the algorithm by DKF simple implementation. The experimental trial of the algorithm is performed on the robocup middle size soccer robot; the system can be used in more general environments.
Abstract: This paper introduces our first efforts of developing a
new team for RoboCup Middle Size Competition. In our robots we
have applied omni directional based mobile system with omnidirectional
vision system and fuzzy control algorithm to navigate
robots. The control architecture of MRL middle-size robots is a three
layered architecture, Planning, Sequencing, and Executing. It also
uses Blackboard system to achieve coordination among agents.
Moreover, the architecture should have minimum dependency on low
level structure and have a uniform protocol to interact with real
robot.
Abstract: RoboCup Rescue simulation as a large-scale Multi
agent system (MAS) is one of the challenging environments for
keeping coordination between agents to achieve the objectives
despite sensing and communication limitations. The dynamicity of
the environment and intensive dependency between actions of
different kinds of agents make the problem more complex. This point
encouraged us to use learning-based methods to adapt our decision
making to different situations. Our approach is utilizing
reinforcement leaning. Using learning in rescue simulation is one of
the current ways which has been the subject of several researches in
recent years. In this paper we present an innovative learning method
implemented for Police Force (PF) Agent. This method can cope
with the main difficulties that exist in other learning approaches.
Different methods used in the literature have been examined. Their
drawbacks and possible improvements have led us to the method
proposed in this paper which is fast and accurate. The Brain
Emotional Learning Based Intelligent Controller (BELBIC) is our
solution for learning in this environment. BELBIC is a
physiologically motivated approach based on a computational model
of amygdale and limbic system. The paper presents the results
obtained by the proposed approach, showing the power of BELBIC
as a decision making tool in complex and dynamic situation.
Abstract: Omni directional mobile robots have been popularly
employed in several applications especially in soccer player robots
considered in Robocup competitions. However, Omni directional
navigation system, Omni-vision system and solenoid kicking
mechanism in such mobile robots have not ever been combined. This
situation brings the idea of a robot with no head direction into
existence, a comprehensive Omni directional mobile robot. Such a
robot can respond more quickly and it would be capable for more
sophisticated behaviors with multi-sensor data fusion algorithm for
global localization base on the data fusion. This paper has tried to
focus on the research improvements in the mechanical, electrical and
software design of the robots of team ADRO Iran. The main
improvements are the world model, the new strategy framework,
mechanical structure, Omni-vision sensor for object detection, robot
path planning, active ball handling mechanism and the new kicker
design, , and other subjects related to mobile robot
Abstract: This paper is described one of the intelligent control method in Autonomous systems, which is called fuzzy control to correct the three wheel omnidirectional robot movement while it make mistake to catch the target. Fuzzy logic is especially advantageous for problems that can not be easily represented by mathematical modeling because data is either unavailable, incomplete or the process is too complex. Such systems can be easily up grated by adding new rules to improve performance or add new features. In many cases , fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the current control method. The fuzzy controller designed here is more accurate and flexible than the traditional controllers. The project is done at MRL middle size soccer robot team.
Abstract: Soccer simulation is an effort to motivate researchers and practitioners to do artificial and robotic intelligence research; and at the same time put into practice and test the results. Many researchers and practitioners throughout the world are continuously working to polish their ideas and improve their implemented systems. At the same time, new groups are forming and they bring bright new thoughts to the field. The research includes designing and executing robotic soccer simulation algorithms. In our research, a soccer simulation player is considered to be an intelligent agent that is capable of receiving information from the environment, analyze it and to choose the best action from a set of possible ones, for its next move. We concentrate on developing a two-phase method for the soccer player agent to choose its best next move. The method is then implemented into our software system called Nexus simulation team of Ferdowsi University. This system is based on TsinghuAeolus[1] team that was the champion of the world RoboCup soccer simulation contest in 2001 and 2002.