Name
Abstract | ||
|---|---|---|
Over the last decade, developments in
unmanned aerial vehicles (UAVs) has greatly increased, and they are being used in many fields including surveillance, crisis management or automated mission planning. This last field implies the search of plans for missions with multiple tasks, UAVs and ground control stations; and the optimization of several objectives, including makespan, fuel consumption or cost, among others. In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model, which is used in the fitness function of the algorithm. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s12652-018-0930-0 | Journal of Ambient Intelligence and Humanized Computing |
Keywords | Field | DocType |
Unmanned air vehicles,Mission planning,Multi-objective optimization,Constraint satisfaction problems | Mathematical optimization,Job shop scheduling,Evolutionary algorithm,Computer science,Multi-objective optimization,Constraint satisfaction problem,Fitness function,Crisis management,Artificial intelligence,Fuel efficiency,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
10.0 | SP6.0 | 1868-5145 |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristian Ramírez-Atencia | 1 | 40 | 7.40 |
| David Camacho | 2 | 331 | 43.45 |