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Handling swarm of UAVs based on evolutionary multi-objective optimization


The fast technological improvements in unmanned aerial vehicles (UAVs) has created new scenarios where a swarm of UAVs could operate in a distributed way. This swarm of vehicles needs to be controlled from a set of ground control stations, and new reliable mission planning systems, which should be able to handle the large amount of variables and constraints. This paper presents a new approach where this complex problem has been modelled as a constraint satisfaction problem (CSP), and is solved using a multi-objective genetic algorithm (MOGA). The algorithm has been designed to minimize several variables of the mission, such as the fuel consumption or the makespan among others. The designed fitness function, used by the algorithm, takes into consideration, as a weighted penalty function, the number of constraints fulfilled for each solution. Therefore, the MOGA algorithm is able to manage the number of constraints fulfilled by the selected plan, so it is possible to maximize in the elitism phase of the MOGA the quality of the solutions found. This approach allows to alleviate the computational effort carried out by the CSP solver, finding new solutions from the Pareto front, and therefore reducing the execution time to obtain a solution. In order to test the performance of this new approach 16 different mission scenarios have been designed. The experimental results show that the approach outperforms the convergence of the algorithm in terms of number of generations and runtime.

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This work has been supported by the next research projects: Airbus Defence and Space (FUAM-076914 and FUAM-076915), UAH 2016/00351/001, EphemeCH (TIN2014-56494-C4-4-P) Spanish Ministry of Economy and Competitivity, CIBERDINE S2013/ICE-3095, both under the European Regional Development Fund FEDER, and RiskTrack project co-funded by the European Union’s Justice Program (2014–2020). The authors would like to acknowledge the support obtained from Airbus Defence and Space, specially from Savier Open Innovation project members: José Insenser, Gemma Blasco and César Castro.

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Correspondence to Cristian Ramirez-Atencia.

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Ramirez-Atencia, C., R-Moreno, M.D. & Camacho, D. Handling swarm of UAVs based on evolutionary multi-objective optimization. Prog Artif Intell 6, 263–274 (2017).

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  • Unmanned aerial vehicles
  • Mission planning
  • Constraint satisfaction problems
  • Multi-objective genetic algorithm