Pedro Lima

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, a method for robot self-localization based on a catadioptric omni-directional sensor is introduced. The method was designed to be applied to fully autonomous soccer robots participating in the middle-size league of RoboCup competitions. It uses natural landmarks of the soccer field, such as field lines and goals, as well as a priori knowledge of the field geometry, to determine the robot position and orientation with respect to a coordinate system whose location is known. The landmarks are processed from an image taken by an omni-directional vision system, based on a camera plus a convex mirror designed to obtain (by hardware) the ground plane bird’s eye view, thus preserving field geometry in the image. Results concerning the method’s accuracy are presented.

This paper describes a modified potential fields method for robot navigation, especially suited for unicycle-type non-holonomic mobile robots. The potential field is modified so as to enhance the relevance of obstacles in the direction of the robot motion. The relative weight assigned to front and side obstacles can be modified by the adjustment of one physically interpretable parameter. The resulting angular speed and linear acceleration of the robot can be expressed as functions of the linear speed, distance and relative orientation to the obstacles. For soccer robots, moving to a desired posture with and without the ball are relevant issues. To enable a soccer robot to dribble a ball, i.e., to move while avoiding obstacles and pushing the ball without losing it, under severe restrictions to ball holding capabilities, a further constraint among the angular speed, linear speed and linear acceleration is introduced. This dribbling behavior has been used successfully in the robots of the RoboCup Middle-Size League ISocRob team.

This work describes a semi-autonomous robot for rescue operations, nicknamed RAPOSA (FOX in English). The robot was designed and built to operate in outdoor environments hostile to the human presence, such as debris resulting from the collapse of built structures, and is targeted to the tele-operated detection of potential survivors using a set of specific sensors whose information is transmitted to a remote human operator. RAPOSA's mechanical structure is composed of a main body and a front body, whose locomotion is supported on tracked wheels, allowing motion even when the robot is upside down. The front body has variable tilting capabilities, providing means to overcome edges higher than the robot main body (e.g., when climbing a stair) and is also useful to grab the lower ground when only the main body has ground contact. This front body has one thermal camera and two web cameras installed. Additional sensors include gas, temperature and humidity sensors, Web cams, light diodes, microphone and loudspeaker. The robot uses wireless communications, with an option for tethered operation. The tether carries both power and communications, with an access point on its end, and can also be used to suspend the robot inside a deep hole. Docking and undocking the robot to the tether is accomplished remotely by the operator with the help of a camera located inside the robot, and represents the most innovative feature of RAPOSA

In this paper, a method for robot self-localization based on a catadioptric omni-directional sensor is introduced. The method uses natural geometric landmarks of the environment. It is assumed that the robot moves on flat surfaces and straight lines can be identified in the surrounding environment image acquired by the catadioptric system. This omni-directional vision system is based on a camera plus a convex mirror designed to obtain (by hardware) the ground plane bird's eye view. Results from the application to a real robot moving on RoboCup soccer field and concerning the method's accuracy are presented

This work introduces a method for robot navigation in structured indoors environments, based on the information of multiple sensors. Guidance control is based on odometry, reset at some time instants by a vision-based self-localization algorithm introduced in previous work. Sonar data is used to avoid and go around obstacles. Results from the application of the complete navigation system to a real robot moving on a RoboCup soccer field are presented.

The SocRob project was born as a challenge for multidisciplinary research on broad and generic approaches for the design of a cooperative robot society, involving Control, Robotics and Artificial Intelligence researchers. In this paper the basic aspects of last year implementation as well as the improvements made meanwhile are briefly recalled and presented. Naturally, a special emphasis is given here to the novel solutions proposed for this year implementation, the results obtained and the expected future developments.

A three-level functional architecture for a team of mobile robots is described in detail, including the definition of the role assigned to each level, the main concepts involved, and the corresponding implementation for each individual robot. The architecture is oriented towards teams of fully autonomous cooperative robots, able to carry out different types of cooperative tasks. Complexity is reduced by the decomposition of team strategies into individual behaviors, which in turn are composed of primitive tasks. Relationships among robots of the team are modeled upon the joint intentions framework. An application to Robotic Soccer and some of its preliminary results are presented.