Abstract: In this article we present a hierarchical model for planning and coordinating firefighting in situations such as RoboCupRescue, where an urban earthquake is simulated. We show that a hierarchical approach brings leverage to the planning process. By using formal decision theoretic models, we also present a more formal analysis of the RoboCupRescue domain. Finally we discuss how our model could be applied and treat further abstractions that may be necessary.
Abstract: Omnidirectional vision is currently an important sensor in robotic research. The catadioptric omnidirectional camera with a hyperbolic convex mirror is a common omnidirectional vision system in the robotics research field as it has many advantages over other vision systems. This paper describes the development and validation of such a system for the RoboCupRescue League simulator USARSim.
After an introduction of the mathematical properties of a real catadioptric omnidirectional camera we give a general overview of the simulation method. We then compare different 3D mirror meshes with respect to quality and system performance. Simulation data also is compared to real omnidirectional vision data obtained on an 4-Legged League soccer field. Comparison is based on using color histogram landmark detection and robot self-localization based on an Extended Kalman filter.
Abstract: In this paper we describe a new approach to make use of
a heterogeneous robot team for the RoboCupRescue League Virtual
Robot competition. We will demonstrate coordinated action between a
flying and a ground robot. The flying robot is used for fast exploration
and allows the operator to find the places where victims are present in
the environment. Due to the fast aggregation of the location error in the
flying robot no precise location of the victim is known. It is the task of
the ground robot to autonomously go the point of interest and to get
an accurate location of the victim, which can be used by human rescue
workers to save the victim. The benefit of this approach is demonstrated
in a small number of experiments. By integrating the abilities of the two
robots the teamís performance is improved.
Abstract: This paper presents the map evaluation methodology developed for the Virtual Robots Rescue competition held as part of RoboCup. The procedure aims to evaluate the quality of maps produced by multi-robot systems with respect to a number of factors, including usability, exploration, annotation and other aspects relevant to robots and first responders. In addition to the design choices, we illustrate practical examples of maps and scores coming from the latest RoboCup contest, outlining strengths and weaknesses of our modus operandi. We also show how a benchmarking methodology developed for a simulation testbed effortlessly and faithfully transfers to maps built by a real robot. A number of conclusions may be derived from the experience reported in this paper and a thorough discussion is offered.
Abstract: The RoboCup is an initiative to promote the development of robotics in a social relevant way. The competition consists of several leagues and it would be benecial if developments in one league could be reused in other leagues. This paper describes the development of a simulation model for a humanoid robot inside USARSim, which could be the basis of synergy between the Rescue Simulation, Soccer Simulation and @Home League. USARSim is an existing 3D simulator based on the Unreal Engine, which provides facilities for good quality rendering, physics simulation, networking, a highly versatile scripting language and a powerful visual editor. This simulator is now extended with the dynamics of a walking robot and validated for the humanoid robot Nao. On this basis many other robotic applications as benchmarked in the RoboCup initiative become possible.
Abstract: Free space detection based on visual clues is an
upcoming approach in robotics. Our working domain is the
Virtual Rescue League of the RoboCup. In this domain efficient
obstacle avoidance is crucial to find victims under challenging
conditions. In this study a machine-learning approach is applied
to distinguish the difference in visual appearance of obstacles
and free space. Omnidirectional camera images are transformed
to bird-eye view, which makes comparison with local occupancy
maps possible. Bird-eye view images are automatically labeled
using Laser Range information, allowing completely autonomous
and continuous learning of accurate color models. Two colorbased
models are compared; a Histogram Method and a Gaussian
Mixture Model. Both methods achieve very good performances,
with results in a high precision and recall on a typical map
from the Rescue League. The Gaussian Mixture Model achieves
the best scores with much less parameters on this map, but is
beaten by the Histogram Method on real data collected by our
Nomad robot. Additionally, the importance of the right color
normalization scheme and model parameters is demonstrated in
Abstract: The Urban Search and Rescue Simulation used for RoboCup lacks realistic response
of laser range finders on smoke. In this paper, the behavior of a Hokuyo and Sick laser range
finder in a smoky environment is studied. The behavior of the lasers is among others a function of
the visibility level, and in this article this function is quantified into an explicit model. This model
is implemented in a simulation environment which is the basis of the Virtual Robot competition
of the RoboCupRescue League. The behavior of both real and virtual laser range finders is
compared in a number of validation tests. The validation tests show that the behavior of the
laser range finders in the simulation is consistent with the real world.
Abstract: The RoboCupRescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (for example, Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.
Abstract: Research on organization of Multiagent Systems (M.A.S.) has shown that by adapting its organization, a M.A.S. is better able to operate in dynamic environments. In this paper we describe an experiment with a M.A.S. that consists of agents where the capability to reorganize is integrated in their coordination mechanism. In the RoboCupRescue simulator we have implemented a M.A.S. where work can be coordinated according to three different coordination styles; direct supervision and standardization of skills with and without a reorganization extension. An experiment shows the effects of unknown workload distribution and incomplete information on the performance of the three styles. Results show significant interaction effects between both workload distribution and coordination mechanism, and completeness of information and coordination mechanism. Furthermore, results show that standardization of skills with reorganization performs better and is more robust to heterogeneous workload distribution and incompleteness of information.
Abstract: Research in the area of Multi-Agent System (MAS) organization has shown that the ability for a MAS to adapt its organizational structure can be beneficial when coping with dynamics and uncertainty in the MASs environment. Different types of reorganization exist, such as changing relations and interaction patterns between agents, changing agent roles and changing the coordination style in the MAS. In this paper we propose a framework for agent Coordination and Reorganization (AgentCoRe) that incorporates each of these aspects of reorganization. We describe both declarative and procedural knowledge an agent uses to decompose and assign tasks, and to reorganize. The RoboCupRescue simulation environment is used to demonstrate how AgentCoRe is used to build a MAS that is capable of reorganizing itself by changing relations, interaction patterns and agent roles.
Abstract: Urban Search and Rescue is a growing area of robotic research. The RoboCup Federation has recognized this, and has created the new Virtual Robots competition to complement its existing physical robot and agent competitions. In order to successfully compete in this competition, teams need to field multi-robot solutions that cooperatively explore and map an environment while searching for victims. This paper presents the results of the first annual RoboCupRescue Virtual competition. It provides details on the metrics used to judge the contestants as well as summaries of the algorithms used by the top four teams. This allows readers to compare and contrast these effective approaches. Furthermore, the simulation engine itself is examined and real-world validation results on the engine and algorithms are offered.