The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.
Ryo MASUDA
Hokkaido University
Koichi KOBAYASHI
Hokkaido University
Yuh YAMASHITA
Hokkaido University
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Ryo MASUDA, Koichi KOBAYASHI, Yuh YAMASHITA, "Dynamic Surveillance by Multiple Agents with Fuel Constraints" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 2, pp. 462-468, February 2020, doi: 10.1587/transfun.2019MAP0011.
Abstract: The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019MAP0011/_p
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@ARTICLE{e103-a_2_462,
author={Ryo MASUDA, Koichi KOBAYASHI, Yuh YAMASHITA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Dynamic Surveillance by Multiple Agents with Fuel Constraints},
year={2020},
volume={E103-A},
number={2},
pages={462-468},
abstract={The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.},
keywords={},
doi={10.1587/transfun.2019MAP0011},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Dynamic Surveillance by Multiple Agents with Fuel Constraints
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 462
EP - 468
AU - Ryo MASUDA
AU - Koichi KOBAYASHI
AU - Yuh YAMASHITA
PY - 2020
DO - 10.1587/transfun.2019MAP0011
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2020
AB - The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.
ER -