TY - GEN
T1 - Multi-Robot Target Search using Probabilistic Consensus on Discrete Markov Chains
AU - Shirsat, Aniket
AU - Elamvazhuthi, Karthik
AU - Berman, Spring
N1 - Funding Information:
This work was supported by ONR Young Investigator Award N00014-16-1-2605 and by the Arizona State University Global Security Initiative.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/4
Y1 - 2020/11/4
N2 - In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots, resulting in a time-varying communication network topology. The proposed strategy is proved to achieve consensus, here defined as agreement on the presence of a static target, with no assumptions on the connectivity of the communication network. Using numerical simulations, we investigate the effect of the robot population size, domain size, and information uncertainty on the consensus time statistics under this scheme. We also validate our theoretical results with 3D physics-based simulations in Gazebo. The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.
AB - In this paper, we propose a probabilistic consensus-based multi-robot search strategy that is robust to communication link failures, and thus is suitable for disaster affected areas. The robots, capable of only local communication, explore a bounded environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and exchange information with neighboring robots, resulting in a time-varying communication network topology. The proposed strategy is proved to achieve consensus, here defined as agreement on the presence of a static target, with no assumptions on the connectivity of the communication network. Using numerical simulations, we investigate the effect of the robot population size, domain size, and information uncertainty on the consensus time statistics under this scheme. We also validate our theoretical results with 3D physics-based simulations in Gazebo. The simulations demonstrate that all robots achieve consensus in finite time with the proposed search strategy over a range of robot densities in the environment.
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U2 - 10.1109/SSRR50563.2020.9292589
DO - 10.1109/SSRR50563.2020.9292589
M3 - Conference contribution
AN - SCOPUS:85099437685
T3 - 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020
SP - 108
EP - 115
BT - 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020
A2 - Marques, Lino
A2 - Khonji, Majid
A2 - Dias, Jorge
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2020
Y2 - 4 November 2020 through 6 November 2020
ER -