Massively multi-robot simulation in stage

Vaughan, Richard

doi:10.1007/s11721-008-0014-4

Cited by 275 publications

(156 citation statements)

References 23 publications

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“…Unfortunately, scalability with respect to the number of robots is not the main concern for the vast majority of multi-robot simulators. Vaughan (2008) proposed a benchmark to study scalability in multi-robot simulators and applied it to the Stage simulator. Pinciroli et al (2012) developed a simulator for swarm robotics by focusing explicitly on the scalability issue which was able to simulate 10 5 robots in real time.…”

Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Section: Microscopic Modelsmentioning

confidence: 99%

Swarm robotics: a review from the swarm engineering perspective

et al. 2013

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“…This tool allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, provides benchmarks that pave the way for objective comparison and competition in the field of grasping. Considering the robotic planning domain, a benchmark tool for multi-robot simulation is presented in [9]. In the field of autonomous mobile robots, a unified benchmark framework for evaluating and comparing motion algorithms for autonomous mobile robots and vehicles is introduced in [10].…”

Section: Related Research Workmentioning

confidence: 99%

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

Sanfilippo

Hatledal

Pettersen

et al. 2018

IEEE J. Oceanic Eng.

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A benchmark framework for advanced control methods of maritime cranes is presented based on the use of the Functional Mockup Interface (FMI). The system integrates different manipulator models, all the corresponding hydraulic systems, various vessels, and the surrounding environment for visualisation. Different control methods can be transparently implemented and tested. A set of routine tests, different cost functions and metrics are provided -taking into account several factors, including position accuracy, energy consumption, quality, and safety for both the cranes and the surrounding environment. The concept of operation profiles (OP) is introduced, allowing for definition of different standard transporting and lifting operations. By considering task-oriented routines, this benchmark suite allows the comparison of different control methods independently from the specific crane model to be controlled.Two alternative control methods for maritime cranes based on the use of artificial intelligence (AI) are extensively compared. The first method is based on the use of genetic algorithms (GA), while the second method involves the use of particle swarm optimisation (PSO). Simulation results are presented for both methods.

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“…Commonly, robotic simulators are discrete-time simulators with a constant time step (e.g., Stage [20], Gazebo [21], ARGoS [8]), whereas network simulators are discrete-event simulators (e.g., ns-2, ns-3, QualNet). The former type of simulation assumes that the time step is fixed at the beginning of the simulation, time advances in equal increments, and the state of the system is updated periodically.…”

Section: Integrated Simulation Frameworkmentioning

confidence: 99%

A framework for realistic simulation of networked multi-robot systems

Kudelski

Cinus

Gambardella

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Networked robotics is an area that integrates multi-robot and network technology. The characteristics and the reliability of the communication environment play a fundamental role shaping and affecting behavior and performance of a mobile multi-robot system. In this context, two basic questions arise: how much the overall performance is affected and how can we investigate this influence? Addressing these two questions, in this paper we present the architecture of an integrated simulation environment that allows for realistic simulation of networked robotic systems. The proposed framework integrates two simulators: a network simulator and a multi-robot simulator. We present two implementations based on the ARGoS simulator for the robotic side, and with ns-2 and ns-3 employed as network simulators. We evaluate the proposed tools, both in isolation and integration, and show that they are able to efficiently simulate systems consisting of hundreds of robots. Moreover, we use the proposed framework to demonstrate the effects of communication on the performance of a mobile multi-robot system performing distributed coordination and task assignment. We compare realistic network simulation with simplified communication models and we study the resulting behavior and performance of the robotic system.

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Massively multi-robot simulation in stage

Cited by 275 publications

References 23 publications

Swarm robotics: a review from the swarm engineering perspective

Swarm robotics: a review from the swarm engineering perspective

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

A framework for realistic simulation of networked multi-robot systems

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