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A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1162)

Abstract

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.

Keywords

Multi-Agent Systems Multi-agent mission planning Extended Colored Traveling Salesperson (ECTSP) Genetic algorithms 

Notes

Acknowledgements

Special thanks to Afshin E. Ameri for developing GUI for the MMT.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Innovation, Design and EngineeringMälardalen UniversityVästeråsSweden

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