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Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter

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Distributed Autonomous Robotic Systems (DARS 2021)

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 22))

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Abstract

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multi-robot formation, even when the communication fails and the tracking data are insufficient for maintaining a stable formation. The filter incorporates, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. By combining communicated data, information about the formation, and sensory detections, the resulting algorithm preserves accuracy in the state estimates despite frequent occurrences of long-duration sensing occlusions, and provides the necessary state information when the communication is sporadic or suffers from short-term outage. Differently from our previous contributions, in which the tracking strategy has only been tested in simulation, in this paper we present the results of experiments with a real multi-robot system. The results confirm that the algorithm enables robust formation maintenance in cluttered environments, under conditions affected by sporadic communication and high measurement uncertainty.

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Acknowledgments

Supported by the JSPS Grant-in-Aid for Scientific Research (A) No. 19H01130, Research Institute for Science and Engineering of Waseda University, JST PRESTO No. JPMJPR1754, and the Top Global University Japan Program of the Ministry of Education, Culture, Sports, Science and Technology. Partially supported by ISR/LARSyS Strategic Funds from FCT project FCT[UID/EEA/5009/2013] and FCT/11145/12/12/2014/S. Additional information about this project can be found here https://www.epfl.ch/labs/disal/research/institutionalroboticsformations/.

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Authors and Affiliations

  1. Department of Modern Mechanical Engineering, Graduate School of Creative Science and Engineering, Waseda University, Tokyo, Japan

    Michiaki Hirayama & Mitsuhiro Kamezaki

  2. Distributed Intelligent Systems and Algorithms Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Alicja Wasik & Alcherio Martinoli

Authors
  1. Michiaki Hirayama
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  2. Alicja Wasik
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  3. Mitsuhiro Kamezaki
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  4. Alcherio Martinoli
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Corresponding author

Correspondence to Michiaki Hirayama .

Editor information

Editors and Affiliations

  1. Mechanical Engineering and Science, Kyoto University, Kyoto, Japan

    Fumitoshi Matsuno

  2. Mechanical Systems Engineering, Nagoya University, Nagoya, Japan

    Shun-ichi Azuma

  3. Computer Science and Information Technology, Hokkaido University, Sapporo, Hokkaido, Japan

    Masahito Yamamoto

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Hirayama, M., Wasik, A., Kamezaki, M., Martinoli, A. (2022). Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_12

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