Abstract
In this paper, a multiple Cycle Sharing Algorithm (CSA) is proposed to solve the Multi-Robot Patrolling Problem (MRPP). In the MRPP, robots visit vertices in a graph continuously. The evaluation metric of the idleness of vertices is considered to evaluate the performance of an algorithm. Minimizing average graph idleness and the graph idleness standard deviation is covered because smaller average idleness means robots visit vertices more often, and smaller standard deviation of the graph idleness means robots visit vertices more regularly. The most effective way to minimize standard deviation is finding a Hamiltonian cycle in a graph. A Hamiltonian cycle visits each vertex exactly once, except for the vertex in which it starts and ends, thus visiting it twice. The solution to the Traveling Salesman Problem is known to minimize the cost of the corresponding Hamiltonian cycle. It is an NP-complete problem. If graph size becomes larger the longer time required for finding the minimum cost cycle; however, by partitioning the graph correctly into multiple sections with cooperative robots, the calculation time can be reduced remarkably.
| Original language | English |
|---|---|
| Title of host publication | 2019 European Conference on Networks and Communications, EuCNC 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 300-304 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781728105468 |
| DOIs | |
| Publication status | Published - 2019 Jun |
| Event | 28th European Conference on Networks and Communications, EuCNC 2019 - Valencia, Spain Duration: 2019 Jun 18 → 2019 Jun 21 |
Publication series
| Name | 2019 European Conference on Networks and Communications, EuCNC 2019 |
|---|
Conference
| Conference | 28th European Conference on Networks and Communications, EuCNC 2019 |
|---|---|
| Country | Spain |
| City | Valencia |
| Period | 19/6/18 → 19/6/21 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Hardware and Architecture
- Safety, Risk, Reliability and Quality
Cite this
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A Multi-Robot Cooperative Patrolling Algorithm with Sharing Multiple Cycles. / Hong, Youngtaek; Kyung, Yeosun; Kim, Seong Lyun.
2019 European Conference on Networks and Communications, EuCNC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 300-304 8802015 (2019 European Conference on Networks and Communications, EuCNC 2019).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A Multi-Robot Cooperative Patrolling Algorithm with Sharing Multiple Cycles
AU - Hong, Youngtaek
AU - Kyung, Yeosun
AU - Kim, Seong Lyun
PY - 2019/6
Y1 - 2019/6
N2 - In this paper, a multiple Cycle Sharing Algorithm (CSA) is proposed to solve the Multi-Robot Patrolling Problem (MRPP). In the MRPP, robots visit vertices in a graph continuously. The evaluation metric of the idleness of vertices is considered to evaluate the performance of an algorithm. Minimizing average graph idleness and the graph idleness standard deviation is covered because smaller average idleness means robots visit vertices more often, and smaller standard deviation of the graph idleness means robots visit vertices more regularly. The most effective way to minimize standard deviation is finding a Hamiltonian cycle in a graph. A Hamiltonian cycle visits each vertex exactly once, except for the vertex in which it starts and ends, thus visiting it twice. The solution to the Traveling Salesman Problem is known to minimize the cost of the corresponding Hamiltonian cycle. It is an NP-complete problem. If graph size becomes larger the longer time required for finding the minimum cost cycle; however, by partitioning the graph correctly into multiple sections with cooperative robots, the calculation time can be reduced remarkably.
AB - In this paper, a multiple Cycle Sharing Algorithm (CSA) is proposed to solve the Multi-Robot Patrolling Problem (MRPP). In the MRPP, robots visit vertices in a graph continuously. The evaluation metric of the idleness of vertices is considered to evaluate the performance of an algorithm. Minimizing average graph idleness and the graph idleness standard deviation is covered because smaller average idleness means robots visit vertices more often, and smaller standard deviation of the graph idleness means robots visit vertices more regularly. The most effective way to minimize standard deviation is finding a Hamiltonian cycle in a graph. A Hamiltonian cycle visits each vertex exactly once, except for the vertex in which it starts and ends, thus visiting it twice. The solution to the Traveling Salesman Problem is known to minimize the cost of the corresponding Hamiltonian cycle. It is an NP-complete problem. If graph size becomes larger the longer time required for finding the minimum cost cycle; however, by partitioning the graph correctly into multiple sections with cooperative robots, the calculation time can be reduced remarkably.
UR - http://www.scopus.com/inward/record.url?scp=85071721741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071721741&partnerID=8YFLogxK
U2 - 10.1109/EuCNC.2019.8802015
DO - 10.1109/EuCNC.2019.8802015
M3 - Conference contribution
AN - SCOPUS:85071721741
T3 - 2019 European Conference on Networks and Communications, EuCNC 2019
SP - 300
EP - 304
BT - 2019 European Conference on Networks and Communications, EuCNC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
ER -