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 RoboCup Rescue 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.

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.

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The use of soft robots in future space exploration is still a far-fetched idea, but an attractive one. Soft robots are inherently compliant mechanisms that are well suited for locomotion on rough terrain as often faced in extra-planetary environments. Depending on the particular application and requirements, the best shape (or body morphology) and locomotion strategy for such robots will vary substantially. Recent developments in soft robotics and evolutionary optimization showed the possibility to simultaneously evolve the morphology and locomotion strategy in simulated trials. The use of techniques such as generative encoding and neural evolution were key to these findings. In this paper, we improve further on this methodology by introducing the use of a novelty measure during the evolution process. We compare fitness search and novelty search in different gravity levels and we consistently find novelty-based search to perform as good as or better than a fitness-based search, while also delivering a greater variety of designs. We propose a combination of the two techniques using fitness-elitism in novelty search to obtain a further improvement. We then use our methodology to evolve the gait and morphology of soft robots at different gravity levels, finding a taxonomy of possible locomotion strategies that are analyzed in the context of space-exploration.

Thanks to advances in both computer science and engineering, the divide between robotics and multi-agent systems is shrinking. Robots are capable of performing an ever wider range of tasks, and there is an increasing need for solutions to high-level problems such as multi-agent coordination. In this paper we examine the problem of finding a robust exploration strategy for a team of mobile robots that takes into account communication limitations. We propose four performance metrics to evaluate and compare existing multi-robot exploration algorithms, and present a role-based approach in which robots either act as explorers or as relays. The result is a complete exploration of the environment in which information is efficiently returned to a central command centre, which is particularly applicable to the domain of rescue robotics.

The RoboCup Rescue competitions have been initiated in 2000. To celebrate 16 years of research and development in this socially relevant initiative this article gives an overview of the experience gained during these competitions. This article provides an overview the stateof-the-art and the lessons learned from the RoboCup Rescue competitions.

This article investigates the effect of incorporating knowledge about the communication possibilities in an exploration algorithm used to map an unknown environment. The mission is to explore a hypothetical disaster site with a small team of robots. The challenge faced by the robot team is to coordinate their actions such that they efficiently explore the environment in their search for victims. The coordination can only be optimal when the robots share the same map. With a limited communication range the map cannot be shared in all circumstances. This article concentrates on the effect of a distributed map, where each robot has only has knowledge of a part of the global map and has no guaranteed connection to the other robot or the operator.

This article investigates the scenario where a small team of robots needs to explore a hypothetical disaster site. The challenge faced by the robot-team is to coordinate their actions such that they efficiently explore the environment in their search for victims. A popular paradigm for the exploration problem is based on the notion of frontiers: the boundaries of the current map from where robots can enter yet unexplored area. Coordinating multiple robots is then about intelligently assigning frontiers to robots. Typically, the assignment of a particular frontier to a particular robot is governed by a cost measure, e.g. the movement costs for the robot to reach the frontier. In more recent approaches these costs are traded off with the potential gain in information if the frontier would be explored by the robot. In this paper we will further investigate the effect of balancing movement costs with information gains while assigning frontiers to robots. In our experiments we will illustrate how various choices for this balance can have a significant impact on the exploratory behavior exposed by the robot team.