RoboCup Rescue Simulation Innovation Strategy

Visser, A.; Ito, Nobuhiro; Kleiner, Alexander

doi:10.1007/978-3-319-18615-3_54

Cited by 18 publications

(3 citation statements)

References 21 publications

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“…The agent simulation league provides virtual agents placed on a map of a city that has been damaged by an earthquake event. Competitors focus on different selfisolated AI problems (e.g., task allocation) provided by the contest developing algorithms for them [11]. In addition, all teams have to give a presentation on their solution, which counts towards their final score.…”

Section: Related Workmentioning

confidence: 99%

The Multi-Agent Programming Contest: A résumé

Ahlbrecht,

Dix,

Fiekas

et al. 2020

Preprint

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The Multi-Agent Programming Contest, MAPC, is an annual event organized since 2005 out of Clausthal University of Technology. Its aim is to investigate the potential of using decentralized, autonomously acting intelligent agents, by providing a complex scenario to be solved in a competitive environment. For this we need suitable benchmarks where agent-based systems can shine. We present previous editions of the contest and also its current scenario and results from its use in the 2019 MAPC with a special focus on its suitability. We conclude with lessons learned over the years.

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Section: Related Workmentioning

confidence: 99%

The Multi-Agent Programming Contest: A résumé

Ahlbrecht,

Dix,

Fiekas

et al. 2020

Preprint

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“…There are very few research efforts in this domain. For example, the Robocop urban search and rescue (USAR) aims to promote research in collaborative problem solving by multiple robots in a city scale arenas (Visser et al 2015). However, the emphasis of this competition is on cooperative mapping, search and rescue, disaster management, coordination etc., rather than coverage and exploration.…”

Section: Related Workmentioning

confidence: 99%

A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale

Zia

Din

Shahzad

et al. 2017

Complex Adapt Syst Model

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Background In this article, we model a behavior-based strategy of autonomous coverage and exploration at the scale of a city with multiple robots. The behavioral components are motivated by Cepeda et al. (Sensors 12 (9): 12772–12797, 2012) and extended to incorporate into a generic cellular-automata based agent model. These agents are representing homogenous robots with reactive control. Deliberative approaches requires large scale map and large memory, which slowdowns the execution. Our approach is reactive and simple, that is, robots have no prior information about the environment and do not generate a route map as they traverse. However, other robots in neighborhood are detected using local sensors. Findings A city-scale map-driven simulation is designed and model’s efficiency is evaluated for different deployment possibilities. It is evidenced that even with this simple model, the agents are able to explore a significant percentage of the environment. Conclusion For a city-scale multi-robotic exploration, our simple but efficient model does not require explicit communication and data sharing (and hence representation and storage of navigated map) because possibility of encountering and influencing another agent is quite low, due to spatial dynamics of the environment.

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“…What was still missing inside the Rescue Simulation League is an environment which is a common ground for both the Agent and Virtual Robot competitions. Inside the Agent competition the focus is on the coordination of rescue teams [20,33]. The competition consists of a simulation environment which resembles a city after an earthquake.…”

Section: Introductionmentioning

confidence: 99%

MRESim, a Multi-robot Exploration Simulator for the Rescue Simulation League

Spirin

Hoog

Visser

et al. 2015

RoboCup 2014: Robot World Cup XVIII

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This paper describes MRESim, a multi-robot exploration simulator which aims to provide a middle ground between the RoboCup Agent and Virtual Robot competitions. A detailed description of this new infrastructure is provided, followed by examples and case studies of successful research outcomes arising from the use of MRESim. Our work on MRESim won the 2014 Infrastructure competition of the RoboCup Rescue Simulation League.

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RoboCup Rescue Simulation Innovation Strategy

Cited by 18 publications

References 21 publications

The Multi-Agent Programming Contest: A résumé

The Multi-Agent Programming Contest: A résumé

A Cognitive Agent-based Model for Multi-Robot Coverage at a City Scale

MRESim, a Multi-robot Exploration Simulator for the Rescue Simulation League

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