TY - GEN
T1 - Robust Localization for Multi-robot Formations
T2 - 15th International Symposium on Distributed Autonomous Robotic Systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021
AU - Hirayama, Michiaki
AU - Wasik, Alicja
AU - Kamezaki, Mitsuhiro
AU - Martinoli, Alcherio
N1 - Funding Information:
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/.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Cooperative localization
KW - Formation control
KW - Multi-robot tracking
KW - Probability hypothesis density filter
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U2 - 10.1007/978-3-030-92790-5_12
DO - 10.1007/978-3-030-92790-5_12
M3 - Conference contribution
AN - SCOPUS:85123280222
SN - 9783030927899
T3 - Springer Proceedings in Advanced Robotics
SP - 148
EP - 162
BT - Distributed Autonomous Robotic Systems - 15th International Symposium, 2022
A2 - Matsuno, Fumitoshi
A2 - Azuma, Shun-ichi
A2 - Yamamoto, Masahito
PB - Springer Nature
Y2 - 1 June 2021 through 4 June 2021
ER -