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In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for multi-agent surveillance systems (the optimal surveillance problem) is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a new model predictive control method for multi-agent surveillance systems is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.2 pp.372-378

- Publication Date
- 2019/02/01

- Publicized

- Online ISSN
- 1745-1337

- DOI
- 10.1587/transfun.E102.A.372

- Type of Manuscript
- Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)

- Category

Koichi KOBAYASHI

Hokkaido University

Mifuyu KIDO

Hokkaido University

Yuh YAMASHITA

Hokkaido University

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Koichi KOBAYASHI, Mifuyu KIDO, Yuh YAMASHITA, "Computationally Efficient Model Predictive Control for Multi-Agent Surveillance Systems" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 2, pp. 372-378, February 2019, doi: 10.1587/transfun.E102.A.372.

Abstract: In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for multi-agent surveillance systems (the optimal surveillance problem) is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a new model predictive control method for multi-agent surveillance systems is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.

URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.372/_p

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@ARTICLE{e102-a_2_372,

author={Koichi KOBAYASHI, Mifuyu KIDO, Yuh YAMASHITA, },

journal={IEICE TRANSACTIONS on Fundamentals},

title={Computationally Efficient Model Predictive Control for Multi-Agent Surveillance Systems},

year={2019},

volume={E102-A},

number={2},

pages={372-378},

abstract={In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for multi-agent surveillance systems (the optimal surveillance problem) is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a new model predictive control method for multi-agent surveillance systems is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.},

keywords={},

doi={10.1587/transfun.E102.A.372},

ISSN={1745-1337},

month={February},}

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TY - JOUR

TI - Computationally Efficient Model Predictive Control for Multi-Agent Surveillance Systems

T2 - IEICE TRANSACTIONS on Fundamentals

SP - 372

EP - 378

AU - Koichi KOBAYASHI

AU - Mifuyu KIDO

AU - Yuh YAMASHITA

PY - 2019

DO - 10.1587/transfun.E102.A.372

JO - IEICE TRANSACTIONS on Fundamentals

SN - 1745-1337

VL - E102-A

IS - 2

JA - IEICE TRANSACTIONS on Fundamentals

Y1 - February 2019

AB - In this paper, a surveillance system by multiple agents, which is called a multi-agent surveillance system, is studied. A surveillance area is given by an undirected connected graph. Then, the optimal control problem for multi-agent surveillance systems (the optimal surveillance problem) is to find trajectories of multiple agents that travel each node as evenly as possible. In our previous work, this problem is reduced to a mixed integer linear programming problem. However, the computation time for solving it exponentially grows with the number of agents. To overcome this technical issue, a new model predictive control method for multi-agent surveillance systems is proposed. First, a procedure of individual optimization, which is a kind of approximate solution methods, is proposed. Next, a method to improve the control performance is proposed. In addition, an event-triggering condition is also proposed. The effectiveness of the proposed method is presented by a numerical example.

ER -