Figures (10)  Tables (6)

    • Figure 1. 

      Schematic diagram of satellite-MAV cooperative observation.

    • Figure 2. 

      Overall framework of the proposed GW-SWCRI method.

    • Figure 3. 

      Algorithm flow of MBM-FPP.

    • Figure 4. 

      Fitness value variation with iterations.

    • Figure 5. 

      Satellite and MAV grid coverage results.

    • Figure 6. 

      Variation of fitness value with iterations.

    • Figure 7. 

      Average optimal MAV cost term vs iteration number.

    • Figure 8. 

      Curve of average optimal MAV distance vs iteration number.

    • Figure 9. 

      Variation of fitness of the three leader wolves with iterations.

    • Figure 10. 

      Fitness variation curves with iterations for different algorithms.

    • Input: task region $ \Omega $, satellite set $ S $, MAV bases $ B $, angle range $ [At{t}_{\min },At{t}_{\max }] $, MAV range $ {D}_{\max } $
      Output: optimal off-nadir angle code $ {b}^{*} $, MAV path set $ R $
      Initialize grey wolf population $ W $
      Evaluate fitness and select $ \alpha $, $ \beta $, and $ \delta $
      for iteration t = 1 to $ {T}_{\max } $ do
      　for wolf $ {w}_{i} $ in $ W $ do
      　　Generate candidate position by GWO
      　　Limit step size and apply reflective boundary handling
      　　Update $ {w}_{i} $
      　end for
      　Recalculate fitness and update $ \alpha $, $ \beta $ and $ \delta $
      end for
      $ {b}^{*} $←$ \alpha $
      $ {g}_{unc} $← uncovered grids generated by $ {b}^{*} $
      $ {V}_{cand} $← candidate waypoints constructed from $ {g}_{unc} $
      Compute score $ F\left(v\right) $ for each $ v $ in $ {V}_{cand} $
      $ V $ ← selected waypoints according to $ F\left(v\right) $
      Assign $ V $ to nearest MAV bases
      for base $ {B}_{m} $ in $ B $ do
      　while assigned waypoints are not empty do
      　　Initialize path $ r $ from $ {B}_{m} $
      　　while feasible waypoint exists do
      　　　Select $ {v}^{*} $ with minimum extension cost $ G\left(v\right) $
      　　　Add $ {v}^{*} $ to $ r $
      　　end while
      　　Add $ r $ to $ R $
      　end while
      end for
      for path $ r $ in $ R $ do
      　Optimize $ r $ by improved 2-opt
      end for
      return $ {b}^{*} $, $ R $

      Table 1. 

      Overall procedure of the GW-SWCRI method.

    • Constellation nameOrbital altitude (km)Inclination (deg)Number of orbital planesNumber of satellites per planePhasing parameter
      Walker130028.5431
      Walker230045451

      Table 1. 

      Initial satellite parameters and constellation composition.

    • Scenario ID Cross-track pointing range (deg) Swath width (km) Imaging half-angle (deg)
      1 [−30, 30] 60 5.711
      2 [−15, 15] 90 8.531

      Table 2. 

      Payload parameters for Scenarios 1 and 2.

    • Scenario IDBase IDX coordinateY coordinate
      11-1101
      11-2201
      11-3301
      11-4401
      22-1125
      22-22550
      22-35025
      22-4251

      Table 3. 

      Distribution of MAV bases in Scenarios 1 and 2.

    • Parameter Value
      Coverage gain weight 1.0
      Boundary/isolated grid gain weight 0.6
      Connected component gain weight 0.5
      Base distance cost weight 0.8
      Distance cost weight 1.0
      Turn penalty weight 0.35
      Path length weight 1.0
      Turn cost weight 0.25
      Flight range safety margin ratio 0.05
      Risk penalty threshold 0.85
      Maximum number of 2-opt iterations 100
      Flight range risk penalty weight 2.0

      Table 4. 

      Initial parameters of the MBM-FPP method.

    • Algorithm name Total running time (s) Average running time per iteration (s)
      GA (population size 20) 1,840.7 9.20
      GWO (population size 20) 1,920 9.60
      GS (population size 20) 1,230.3 6.15
      GA (population size 50) 4,969.9 24.85

      Table 5. 

      Running time of different algorithms.