Multi-agent path planning problem under a multi-objective optimization framework

Izabela Nielsen; Grzegorz Bocewicz; Subrata Saha

doi:10.1007/978-3-030-53829-3_1

Multi-agent path planning problem under a multi-objective optimization framework

Izabela Nielsen, Grzegorz Bocewicz, Subrata Saha^*

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

4 Citations (Scopus)

Abstract

In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.

Original language	English
Title of host publication	Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020
Editors	Sara Rodríguez González, Javier Prieto, Alfonso González-Briones, Arkadiusz Gola, George Katranas, Michela Ricca, Roussanka Loukanova, Roussanka Loukanova
Number of pages	10
Publisher	Springer VS
Publication date	2021
Pages	5-14
ISBN (Print)	978-3-030-53828-6
ISBN (Electronic)	978-3-030-53829-3
DOIs	https://doi.org/10.1007/978-3-030-53829-3_1
Publication status	Published - 2021
Event	17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020 - L´Aquila, Italy Duration: 17 Jun 2020 → 19 Jun 2020

Conference

Conference	17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020
Country/Territory	Italy
City	L´Aquila
Period	17/06/2020 → 19/06/2020

Series	Advances in Intelligent Systems and Computing
Volume	1242 AISC
ISSN	2194-5357

Bibliographical note

Publisher Copyright:
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Mixed-integer programming
Multi-agent path planning
Search and rescue

Access to Document

10.1007/978-3-030-53829-3_1

AUB Link

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Cite this

Nielsen, I., Bocewicz, G., & Saha, S. (2021). Multi-agent path planning problem under a multi-objective optimization framework. In S. Rodríguez González, J. Prieto, A. González-Briones, A. Gola, G. Katranas, M. Ricca, R. Loukanova, & R. Loukanova (Eds.), Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020 (pp. 5-14). Springer VS. https://doi.org/10.1007/978-3-030-53829-3_1

Nielsen, Izabela ; Bocewicz, Grzegorz ; Saha, Subrata. / Multi-agent path planning problem under a multi-objective optimization framework. Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020. editor / Sara Rodríguez González ; Javier Prieto ; Alfonso González-Briones ; Arkadiusz Gola ; George Katranas ; Michela Ricca ; Roussanka Loukanova ; Roussanka Loukanova. Springer VS, 2021. pp. 5-14 (Advances in Intelligent Systems and Computing, Vol. 1242 AISC).

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abstract = "In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.",

keywords = "Mixed-integer programming, Multi-agent path planning, Search and rescue",

author = "Izabela Nielsen and Grzegorz Bocewicz and Subrata Saha",

note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020 ; Conference date: 17-06-2020 Through 19-06-2020",

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Nielsen, I, Bocewicz, G & Saha, S 2021, Multi-agent path planning problem under a multi-objective optimization framework. in S Rodríguez González, J Prieto, A González-Briones, A Gola, G Katranas, M Ricca, R Loukanova & R Loukanova (eds), Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020. Springer VS, Advances in Intelligent Systems and Computing, vol. 1242 AISC, pp. 5-14, 17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020, L´Aquila, Italy, 17/06/2020. https://doi.org/10.1007/978-3-030-53829-3_1

Multi-agent path planning problem under a multi-objective optimization framework. / Nielsen, Izabela; Bocewicz, Grzegorz; Saha, Subrata.
Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020. ed. / Sara Rodríguez González; Javier Prieto; Alfonso González-Briones; Arkadiusz Gola; George Katranas; Michela Ricca; Roussanka Loukanova; Roussanka Loukanova. Springer VS, 2021. p. 5-14 (Advances in Intelligent Systems and Computing, Vol. 1242 AISC).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Multi-agent path planning problem under a multi-objective optimization framework

AU - Nielsen, Izabela

AU - Bocewicz, Grzegorz

AU - Saha, Subrata

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2021

Y1 - 2021

N2 - In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.

AB - In this study, a mixed-integer programming formulation is developed for a team of homogeneous sensing agents under a bi-objective optimization framework to solve a discrete open-loop centralized multi-agent search and rescue path planning problem. The first objective represents the maximization of probability of target detection to ensure the success of mission planning and the second objective represents minimization of the cumulative path length of all the agents to ensure resource utilization and ensure adequate area coverage. A two-phase fuzzy programming technique is used to find the Pareto optimal solution. Numerical experiments are conducted with CPLEX to evaluate the effectiveness of the solution procedure with varying number of agents, and the impact of the size of a grid-based rectangular map with a sparsely distributed non-cooperative finite number of stationary targets.

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DO - 10.1007/978-3-030-53829-3_1

M3 - Article in proceeding

AN - SCOPUS:85089613421

SN - 978-3-030-53828-6

T3 - Advances in Intelligent Systems and Computing

SP - 5

EP - 14

BT - Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020

A2 - Rodríguez González, Sara

A2 - Prieto, Javier

A2 - González-Briones, Alfonso

A2 - Gola, Arkadiusz

A2 - Katranas, George

A2 - Ricca, Michela

A2 - Loukanova, Roussanka

PB - Springer VS

T2 - 17th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2020

Y2 - 17 June 2020 through 19 June 2020

ER -

Nielsen I, Bocewicz G, Saha S. Multi-agent path planning problem under a multi-objective optimization framework. In Rodríguez González S, Prieto J, González-Briones A, Gola A, Katranas G, Ricca M, Loukanova R, Loukanova R, editors, Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference, DCAI 2020. Springer VS. 2021. p. 5-14. (Advances in Intelligent Systems and Computing, Vol. 1242 AISC). doi: 10.1007/978-3-030-53829-3_1

Multi-agent path planning problem under a multi-objective optimization framework

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