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Versatile and Massive Experimentation of Robot Swarms in Industrial Scenarios

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Robot 2019: Fourth Iberian Robotics Conference (ROBOT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1093))

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Abstract

This paper aims to present an automated experimentation platform designed to work with multiple mobile robots into industrial scenarios using Robot Operating System (ROS). Robot swarm is a system with complexity proportional of group size, expensive and with arduous setup. Virtual environments can be used to expedite the testing, but also are very difficult due to a hard-work to configure each robot. The proposed platform is a tool set to easily configure the experimentation environment, aiming the swarm tasks, with most popular perception systems, absolute or relative localization reference, and position controllers. The user specifies in tool only the amount of robots required, sensor and function, without having to configure each robot individually for the simulation. This paper presents examples to multi-robot setup in industrial scenarios, through a simple parameterization script. The memory consumption is demystified to given to allow the estimation of computational resources necessary to perform the practical experimentation.

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Notes

  1. 1.

    http://www.coppeliarobotics.com/.

  2. 2.

    http://gazebosim.org/.

  3. 3.

    https://www.argos-sim.info/.

  4. 4.

    http://wiki.ros.org/swarm_stage_ros.

  5. 5.

    https://github.com/VivianCremerKalempa/swarm_stage_ros.

  6. 6.

    https://www.youtube.com/channel/UCztalFc6fapGIhQN2Q0jCuw.

  7. 7.

    https://www.selenic.com/smem/.

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Correspondence to Vivian Cremer Kalempa .

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Kalempa, V.C., Teixeira, M.A.S., de Oliveira, A.S. (2020). Versatile and Massive Experimentation of Robot Swarms in Industrial Scenarios. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-36150-1_23

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