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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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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