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Miloradović, Branko
Publikasjoner (4 av 4) Visa alla publikasjoner
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2020). A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems. In: Parlier G.; Liberatore F.; Demange M. (Ed.), Operations Research and Enterprise Systems: (pp. 109-134). Springer, Cham
Åpne denne publikasjonen i ny fane eller vindu >>A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems
2020 (engelsk)Inngår i: Operations Research and Enterprise Systems / [ed] Parlier G.; Liberatore F.; Demange M., Springer, Cham , 2020, s. 109-134Kapittel i bok, del av antologi (Annet vitenskapelig)
Abstract [en]

Multi-Agent Systems (MASs) have received great attention from scholars and engineers in different domains, including computer science and robotics. MASs try to solve complex and challenging problems (e.g., a mission) by dividing them into smaller problem instances (e.g., tasks) that are allocated to the individual autonomous entities (e.g., agents). By fulfilling their individual goals, they lead to the solution to the overall mission. A mission typically involves a large number of agents and tasks, as well as additional constraints, e.g., coming from the required equipment for completing a given task. Addressing such problem can be extremely complicated for the human operator, and several automated approaches fall short of scalability. This paper proposes a genetic algorithm for the automation of multi-agent mission planning. In particular, the contributions of this paper are threefold. First, the mission planning problem is cast into an Extended Colored Traveling Salesperson Problem (ECTSP), formulated as a mixed integer linear programming problem. Second, a precedence constraint reparation algorithm to allow the usage of common variation operators for ECTSP is developed. Finally, a new objective function minimizing the mission makespan for multi-agent mission planning problems is proposed.

sted, utgiver, år, opplag, sider
Springer, Cham, 2020
Emneord
Multi-Agent Systems, Multi-agent mission planning, Extended Colored Traveling Salesperson (ECTSP), Genetic algorithms
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-46604 (URN)10.1007/978-3-030-37584-3_6 (DOI)2-s2.0-85076881781 (Scopus ID)978-3-030-37584-3 (ISBN)
Prosjekter
Future factories in the CloudAggregate Farming in the Cloud
Tilgjengelig fra: 2019-12-20 Laget: 2019-12-20 Sist oppdatert: 2020-01-02bibliografisk kontrollert
Frasheri, M., Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2020). GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem.
Åpne denne publikasjonen i ny fane eller vindu >>GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem
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2020 (engelsk)Rapport (Annet vitenskapelig)
Abstract [en]

Multi-robot systems can be prone to failures during plan execution, depending on the harshness of the environment they are deployed in. As a consequence, initially devised plans may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. Two main approaches emerge as possible solutions, a global re-planning technique using a centralized planner that will redo the task allocation with the updated world state information, or a decentralized approach that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots.The former approach produces an overall better solution, while the latter is less computationally expensive.The goal of this paper is to exploit the benefits of both approaches, while minimizing their drawbacks. To this end, we propose a hybrid approach {that combines a centralized planner with decentralized multi-agent planning}. In case of an agent failure, the local plan reparation algorithm tries to repair the plan through agent negotiation. If it fails to re-allocate all of the pending tasks, the global re-planning algorithm is invoked, which re-allocates all unfinished tasks from all agents.The hybrid approach was compared to planner approach, and it was shown that it improves on the makespan of a mission in presence of different numbers of failures,as a consequence of the local plan reparation algorithm.

Emneord
Multi-Agent Systems, Autonomous Agents, Centralized Planning, Decentralized Planning
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-47902 (URN)
Tilgjengelig fra: 2020-05-06 Laget: 2020-05-06 Sist oppdatert: 2020-05-18bibliografisk kontrollert
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). Extended colored traveling salesperson for modeling multi-agent mission planning problems. In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems: . Paper presented at 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019 (pp. 237-244). SciTePress
Åpne denne publikasjonen i ny fane eller vindu >>Extended colored traveling salesperson for modeling multi-agent mission planning problems
2019 (engelsk)Inngår i: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, SciTePress , 2019, s. 237-244Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In recent years, multi-agent systems have been widely used in different missions, ranging from underwater to airborne. A mission typically involves a large number of agents and tasks, making it very hard for the human operator to create a good plan. A search for an optimal plan may take too long, and it is hard to make a time estimate of when the planner will finish. A Genetic algorithm based planner is proposed in order to overcome this issue. The contribution of this paper is threefold. First, an Integer Linear Programming (ILP) formulation of a novel Extensive Colored Traveling Salesperson Problem (ECTSP) is given. Second, a new objective function suitable for multi-agent mission planning problems is proposed. Finally, a reparation algorithm to allow usage of common variation operators for ECTSP has been developed. 

sted, utgiver, år, opplag, sider
SciTePress, 2019
Emneord
Colored traveling salesperson (CTSP), Genetic algorithms, Multi-agent mission planning, Integer programming, Operations research, Software agents, Human operator, Integer Linear Programming, Mission planning, Mission planning problem, Objective functions, Traveling salesperson problem, Variation operator, Multi agent systems
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-43305 (URN)2-s2.0-85064712559 (Scopus ID)9789897583520 (ISBN)
Konferanse
8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019
Tilgjengelig fra: 2019-05-09 Laget: 2019-05-09 Sist oppdatert: 2019-10-01bibliografisk kontrollert
Miloradović, B., Frasheri, M., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model. In: PRIMA 2019: Principles and Practice of Multi-Agent Systems. Paper presented at The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy (pp. 478-486).
Åpne denne publikasjonen i ny fane eller vindu >>TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model
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2019 (engelsk)Inngår i: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, s. 478-486Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.

HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-46316 (URN)10.1007/978-3-030-33792-6_32 (DOI)2-s2.0-85076411190 (Scopus ID)978-3-030-33791-9 (ISBN)
Konferanse
The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy
Prosjekter
DPAC - Dependable Platforms for Autonomous systems and ControlUnicorn -Sustainable, peaceful and efficient robotic refuse handlingAggregate Farming in the Cloud
Tilgjengelig fra: 2019-12-12 Laget: 2019-12-12 Sist oppdatert: 2020-05-07bibliografisk kontrollert
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