TY - GEN
T1 - Performance of the asynchronous consensus based bundle algorithm in lossy network environments
AU - Rantanen, Matthew
AU - Modares, Jalil
AU - Mastronarde, Nicholas
AU - Ghanei, Farshad
AU - Dantu, Karthik
N1 - Publisher Copyright:
©2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - We study multi-agent task allocation where multiple tasks must be divided among multiple autonomous robots. Algorithms for solving such problems are typically developed under the assumption of perfect communication, without considering the lossy nature of the underlying wireless network. In this paper, leveraging a sophisticated unmanned aerial vehicle (UAV) network simulation platform, we investigate the sensitivity of a well-known decentralized task allocation framework to realistic communication constraints.In particular, we use the University at Buffalo's Airborne Networking and Communications (UB-ANC) Emulator to demonstrate that the Asynchronous Consensus Based Bundle Algorithm (ACBBA) deviates from its desired theoretical behavior when it is deployed in a realistic (lossy) network setting, especially as the number of agents (UAVs) and number of tasks increase. This may manifest in the form of the same task being assigned to multiple agents and/or some tasks not being assigned at all.
AB - We study multi-agent task allocation where multiple tasks must be divided among multiple autonomous robots. Algorithms for solving such problems are typically developed under the assumption of perfect communication, without considering the lossy nature of the underlying wireless network. In this paper, leveraging a sophisticated unmanned aerial vehicle (UAV) network simulation platform, we investigate the sensitivity of a well-known decentralized task allocation framework to realistic communication constraints.In particular, we use the University at Buffalo's Airborne Networking and Communications (UB-ANC) Emulator to demonstrate that the Asynchronous Consensus Based Bundle Algorithm (ACBBA) deviates from its desired theoretical behavior when it is deployed in a realistic (lossy) network setting, especially as the number of agents (UAVs) and number of tasks increase. This may manifest in the form of the same task being assigned to multiple agents and/or some tasks not being assigned at all.
KW - Decentralized task allocation
KW - Unmanned aerial vehicles (UAVs)
KW - Wireless networking
UR - https://www.scopus.com/pages/publications/85053636179
U2 - 10.1109/SAM.2018.8448984
DO - 10.1109/SAM.2018.8448984
M3 - Conference contribution
AN - SCOPUS:85053636179
SN - 9781538647523
T3 - Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
SP - 311
EP - 315
BT - 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PB - IEEE Computer Society
T2 - 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Y2 - 8 July 2018 through 11 July 2018
ER -