Table 1.
The nomenclature.
Fig 1.
Wind direction.
Fig 2.
Dubins path.
Fig 3.
Relationship between speed vectors.
Fig 4.
Ground speed vector at point X (100,330).
Fig 5.
Encoding of chromosome.
Fig 6.
The pseudocode of crossover operator.
Fig 7.
Crossover process of two chromosomes.
Fig 8.
The pseudocode of target mutation operator.
Fig 9.
The pseudocode of angle mutation operator.
Fig 10.
The pseudocode of UAV mutation operator.
Fig 11.
Process of UAV mutation for chromosome.
Fig 12.
The pseudocode of the GA-based optimization algorithm.
Fig 13.
Optimal flight path for visiting three targets in a windless environment.
Table 2.
UAV flight times along a fixed path under different winds.
Table 3.
UAV flight time in different visiting orders and different winds.
Fig 14.
Flight path using minimum time for U1 to visit three targets under four different winds.
Table 4.
Parameter settings.
Fig 15.
Minimum time in east wind field with wind speed of 5 m/s obtained by using different population sizes and crossover and mutation probabilities “Table in S1 Table”.
Fig 16.
Effect of crossover and mutation probabilities on the results of the algorithm under different population sizes “Table in S2 Table”.
Fig 17.
Algorithm solving process when given two population sizes and three kinds of crossover and mutation probability configurations “Table in S3 Table”.
Table 5.
Results of task allocation and path planning under four different winds.
Fig 18.
Minimum time paths for two UAVs to visit three targets in east wind with different wind speeds.
Table 6.
Results of task allocation and path planning in East wind at different wind speeds.