TY - GEN
T1 - On systems of UAVs for persistent security presence
AU - Kim, Minjun
AU - Morrison, James R.
N1 - Funding Information:
* The work was supported by National Research Foundation of Korea NRF-2016R1A2B4010132 Minjun Kim and James R. Morrison are with the Department of Industrial and Systems Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701, Republic of Korea. *Corresponding author (e-mail: james.morrison@kaist.edu; homepage: http://xS3D.kaist.edu).
Funding Information:
The work was supported by National Research Foundation of Korea NRF-2016R1A2B4010132
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - We develop a task allocation method for persistent UAV security presence (PUSP). UAVs accompany customers and thereby provide security services to them. Key features incorporated are randomness in the arrival of customers and travel durations. We formalize our system as a general network consisting of nodes, arcs, UAVs and routes. From the network, we automatically generate a Markov decision process (MDP) model and simulator. The MDP formulation can be solved exactly only for small problems. In such cases, we employ classic value iteration to obtain optimal polices. To address larger systems consisting of more resources, we develop a greedy task assignment heuristic (GTAH) and simplified MDP heuristics (SMH). Numerical studies demonstrate that the GTAH is approximately 10% suboptimal and that the SMH is about 4% suboptimal with regard to small-scale problems. For larger problems (~1090 states), the performance of the SMH is approximately 3% better than that of the GTAH.
AB - We develop a task allocation method for persistent UAV security presence (PUSP). UAVs accompany customers and thereby provide security services to them. Key features incorporated are randomness in the arrival of customers and travel durations. We formalize our system as a general network consisting of nodes, arcs, UAVs and routes. From the network, we automatically generate a Markov decision process (MDP) model and simulator. The MDP formulation can be solved exactly only for small problems. In such cases, we employ classic value iteration to obtain optimal polices. To address larger systems consisting of more resources, we develop a greedy task assignment heuristic (GTAH) and simplified MDP heuristics (SMH). Numerical studies demonstrate that the GTAH is approximately 10% suboptimal and that the SMH is about 4% suboptimal with regard to small-scale problems. For larger problems (~1090 states), the performance of the SMH is approximately 3% better than that of the GTAH.
KW - Automatic generation process
KW - Heuristic
KW - Markov decision process
KW - Network modeling
KW - Persistent UAV service
KW - Task allocation
UR - http://www.scopus.com/inward/record.url?scp=85071882226&partnerID=8YFLogxK
U2 - 10.1109/ICUAS.2019.8797863
DO - 10.1109/ICUAS.2019.8797863
M3 - Conference contribution
AN - SCOPUS:85071882226
T3 - 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
SP - 238
EP - 245
BT - 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 June 2019 through 14 June 2019
ER -