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  • 1.
    Miloradović, Branko
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering.
    A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems2020In: Operations Research and Enterprise Systems / [ed] Parlier G.; Liberatore F.; Demange M., Springer, Cham , 2020, p. 109-134Chapter in book (Other academic)
    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.

  • 2.
    Miloradović, Branko
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Extended colored traveling salesperson for modeling multi-agent mission planning problems2019In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, SciTePress , 2019, p. 237-244Conference paper (Refereed)
    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. 

  • 3.
    Miloradović, Branko
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Frasheri, Mirgita
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Curuklu, Baran
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ekström, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Papadopoulos, Alessandro
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model2019In: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, p. 478-486Conference paper (Refereed)
    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.

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