Manual RoboCup 2001: Robot Soccer World Cup V

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Mapping and the evaluation of the generated maps is another research area that is important for the league. The Fiducial method for 2D grid map evaluation [12] has recently been extended to 3D maps, using data from the RoboCup Rescue competition [13]. The challenges seen in the DRC Trials were developed by members of the League organizing committee while one of the teams that qualified for the finals, Team ViGIR [15], consisted of many members of Team Hector, one of the most successful teams in the League. These focus on test apparatuses that are easy to build, yield statistically significant results, exercise operationally relevant capabilities and that are easy for suitable robots to attempt and yet can challenge even the most capable robots.

This crossover aims to showcase advances in human technologies in disaster scenarios, provide an evolving benchmark for disaster relief that requires more dexterity than standard wheeled. The initial set of tasks for this demonstration would focus on using human tools in human environments—analogous to the Home league. The task environment, however, would replicate aspects of the Rescue league. The RoboCup Rescue Simulation League RSL aims to develop simulators that form the infrastructure of the simulation system and emulate realistic phenomena predominant in disasters and it aims to develop intelligent agents and robots that are given the capabilities of the main actors in a disaster response scenario.

The RoboCup Simulation League has two major competitions which will be described in the subsequent sections. The two competitions share the Infrastructure competition, which is intended to stimulate the further development of the league with new challenges. Champions of the league are recognized at the League's wiki7 and get the chance to publish their contribution [16, 17] in the Springer Lecture Notes series.

This humanoid challenge was based on a dedicated version of the Gazebo simulator, which in also has become the basis of the RoboCup Rescue Virtual Robot competition [19]. RoboCup Virtual Robot competitions are being held since [20].


The competition attracts mainly academic teams from universities, some even with teams competing in both the RoboCup Rescue Robot and Simulation League. In the competition reached across and attracted high school teams with prior experience in the RoboCup Junior Rescue community; performing precisely the bridging function intended for the Rapidly Manufactured Robot League.

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The main challenge for the teams is the control of a large team of robots typically eight by a single operator. This is still state-of-the art; the only real comparison is the champion of the Magic competition [22], where 14 robots were controlled by two operators. In simulation it was demonstrated that a single operator is able to control a maximum of 24 robots [23]. The single operator has to use high-level commands such as the areas to be searched, routes to be followed, etc. Due to poor lighting and the number of occlusions, the conditions are generally not favorable for automatic victim detection, and manual conformation is always needed.

This means that the workload for the operator is quite high, providing an advantage for the teams which are able to automate the decision making within the robot team as far as possible, and only involve the operator when needed. The shared map generated by the robots during the competition has a central role in the coordination of such large robot teams.

The shared map is where the distributed sensor information is collected and registered, by each robot independently. The information has to be sent via often unreliable communication links [25], so the robot has selected which information is to be broadcasted the robots have a need to know what could be of interest for its teammates and the operator.

The registration process is asynchronous; some information may arrive at the basestation even minutes after the actual observation [26]. There is no guarantee that the operator has time to look at this information directly, which implies that the map within the user interface has to be interactive and should allow the operator to call back observations that were made at any point of interest independent of when the observation was made and by which robot.

At the same time the registration process should keep the map clean no false positives or wrong associations , because it is the area where the coordination of the team behaviors is done. Since the beginning of the competition [20], a number of challenging disaster environments have been created. Already at the RoboCup a quite large world was used, which had a street scenario, an office scenario and a hedge maze in the garden, as illustrated in Fig.

In later competitions a large disaster area with a railway station at a waterfront was used. These environments were based on the Unreal Engine 2 UT With the introduction of the Unreal Engine 3 UDK even larger and more detailed environments could be created. For instance, in the competition a world with very dynamic lighting with moving shadows was introduced, as illustrated in Fig. In the outdoor worlds were already so large that only teams of combined air- and ground-robots could explore the disaster site.

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To be able to control explore these large environments not only improvements of the user interface for the operator were needed, but the teams also increased the. For instance, several well-known methods [27, 28] were applied to be able to automatically recognize victims [29, 30]. To make pure teleoperation of robots based on visual feedback more difficult, indoor environments were often filled with smoke which is realistic in disaster scenarios.

Many publications related to this competition were published, some with quite high impact [21, 33, 34]. The subjects were as diverse as walking robots, design of test arenas and mapping algorithms. The goal of the RoboCup Rescue Agent competition is to compare the performance of different algorithms for coordinating and controlling a team of physical agents performing disaster mitigation in a simulated city [35]. The goal of teams participating in the competition is to provide a software system that reacts to a simulated disaster situation by coordinating a group of agents.

This goal leads to challenges such as the exploration of large-scale environments in order to localize fire-fronts and victims, as well as the scheduling of time-critical rescue missions. Agents have only a limited amount of communication bandwidth they can use to coordinate with each other [36]. The problem cannot be addressed by a single entity, but has to be solved by a multi-agent system.

Moreover, the simulated environment is highly dynamic and only partially observable by a single agent. Agents have to plan and decide their actions asynchronously in real-time. The agent competition consists of a simulation platform which resembles a city after an earthquake. Such a simulation of the city of Kobe is depicted by Fig. Into this. For this purpose, agents may take on heterogeneous roles such as police force, fire brigade, and ambulance team, that all have different capabilities. Several overview articles are written on the coordination and task allocation research performed with the RoboCup Rescue Agent simulator [37, 38].

As indicated by Ferreira et al. Inspired by the influential paper by Murphy et al.

Most important, as implemented as task for the ambulance agents in the Rescue Simulation Agent competition, is to reduce the amount of time a victim is entrapped. Within the last years, there were several techniques for multi-agent strategy planning and team coordination introduced, such as decentralized communicating POMDPs [45], distributed constraint optimization [46], auction based methods [47] and evolutionary learning [48, 49].

Recently, this was extended with work on weighted synergy graphs [50], Tractable Higher Order Potentials constraints [51] and fluid team allocations [52]. Furthermore, there has been substantial work on building information infrastructure and decision support systems for enabling incident commanders to efficiently coordinate rescue teams in the field [53]. In the simulation league has initiated RMasBench, a new type of challenge having the goal to focus on the strategic decisions instead of the tactical decisions [54].

The idea is to extract from the entire problem addressed by the agents certain aspects such as task allocation, team formation, and route planning, and to present these sub problems in an isolated manner as stand-alone problem scenarios with an abstract interface. As a consequence, participating teams are more free to focus on their research without having to deal with low-level issues.

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In the challenge is rephrased as Technical Challenge, which the same intention to abstract away from the low-level tactical decisions, but this time facilitated by an Agent Develop Framework [55]. The future of the Virtual Robot competition was redefined at the Future of Rescue Simulation workshop.

One of the goals of the workshop was to define a roadmap for the development of the league for the coming years. The centaur design could also be a good combination of mobility and manipulation capabilities in the RoboCup Virtual Robot competition, as demonstrated at the workshop See Fig. As a result of the workshop the challenge of the Virtual Robot Competition is refined, which is reflected in a new rules document [57].

Starting in the RRL is adopting a new scheme for the competition. The test method apparatuses will be arranged into lanes and teams will be invited to run their robots multiple times across the lanes. By running these tests in parallel, rigorous measurements of capabilities can be obtained in isolation. Each of those four areas consists of five tests, which often correspond to one of the standard ASTM test methods. Figure 8 shows an overview of how the tests are laid out in the arena. In the preliminaries the Best-in-Class winners in the areas of mobility, dexterity and exploration will be determined as well as the overall best teams that will progress to the finals.

For each area three Best-in-Class certificates are awarded: Best-in-Class Small Robot for robots entering the tests through a 60cm square , Best-in-Class Autonomous Robots for robots performing without operator intervention and the general Best-in-Class certificates open to all teams.

University of Bonn, Computer Science VI, Autonomous Intelligent Systems

In the finals the test elements will be combined such that two big arenas are formed. The finalists will then search in there for simulated victims by traversing the various test elements within a single run.

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Running the competition with this new scheme enables us to conduct challenging and fair competitions that emphasize tasks that are of actual value for USAR applications. As part of the emphasis on the dissemination and collaborative development of technologies for response robotics, from on the Team Description Papers TDP have an updated template covering more technical aspects of the robotic solutions.

The goal of this update is to better allow teams to express and share the novel aspects of their entries. The TDPs of all participating teams will be published online9 and thus be accessible to the general public. More details about the new way the league is run can be found in the rules document [61]. In the last 16 years the RoboCup Rescue community has proven that work on this grand challenge [2] is fruitful. Teams from all over the world are now working on this socially relevant application, evolving their initial hardware designs to very versatile robots. Also the perception, planning and control of the robots have been substantial improved, which makes it possible to autonomously navigate through the disaster area and find victims in difficult circumstances.

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