  Grassland restoration is a critical means to safeguard grassland ecological
degradation. To alleviate the extensive human labors and boost the restoration
efficiency, UAV is promising for its fully automatic capability yet still waits
to be exploited. This paper progresses this emerging technology by explicitly
considering the realistic constraints of the UAV and the grassland degradation
while planning the grassland restoration. To this end, the UAV-enabled
restoration process is first mathematically modeled as the maximization of
restoration areas of the UAV under the limited battery energy of UAV, the grass
seeds weight, the number of restored areas, and the corresponding sizes. Then
we analyze that, by considering these constraints, this original problem
emerges two conflict objectives, namely the shortest flight path and the
optimal areas allocation. As a result, the maximization of restoration areas
turns out to be a composite of a trajectory design problem and an areas
allocation problem that are highly coupled. From the perspective of
optimization, this requires solving two NP-hard problems of both the traveling
salesman problem (TSP) and the multidimensional knapsack problem (MKP) at the
same time. To tackle this complex problem, we propose a cooperative
optimization algorithm, called CHAPBILM, to solve those two problems
interlacedly by utilizing the interdependencies between them. Multiple
simulations verify the conflicts between the trajectory design and areas
allocation. The effectiveness of the cooperative optimization algorithm is also
supported by the comparisons with traditional optimization methods which do not
utilize the interdependencies between the two problems. As a result, the
proposed algorithm successfully solves the multiple simulation instances in a
near-optimal way.
