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... known neighboring points. The key idea behind BI is to: (1) perform linear interpolation along each line of latitude in the West-East direction; (2) normalize the two partial weights for each point; and finally (3) perform a linear interpolation along each line of longitude in the perpendicular (i.e., the South-North) direction, as it is shown in Fig. 5. Although each step is linear in the sampled values and in the position, the interpolation as a whole is not linear but rather quadratic in the sample location. Suppose that we want to find the value of the unknown function f at the point P = (x, y) where f is the elevation at point P, x and y are the lati- tude and longitude of the ...
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... this approach, 3D coverage path planning is quantified for skip (i.e., due to missed application) and/or overlap areas (i.e., due to double application). A coverage path planning consists of a number of headland paths/polygons and a number of parallel field tracks, as it is shown in Fig. 5(a), for a hypothesized testing field terrain of 200 m 2 flat area with a half cylinder of radius 10 m on its center to provide a simple terrain with varying sloping. The plan is generated for an effective operating width w = 1 m and the driving angle θ = 90°. Each field track consists of a number of segments, each of length l chosen in a ...
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... 1 m and the driving angle θ = 90°. Each field track consists of a number of segments, each of length l chosen in a way to give sufficient accuracy when represented in 3D. To numerically measure the distance between tracks in 3D, a 3D cylinder of radius equal to the vehicle's operating width, w, is generated around each segment, as it is shown in Fig. 5(b). Then a fine grid is generated and projected into the field terrain. Finally, the total number of points which are located inside the 3D cylinder, N, is determined and divided by the total number of points located in the entire field, N f , and used as an estimate of the coverage efficiency, given by Eq. ...
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In this paper, we present a three-layer distributed control structure with certain centralization mechanism to generate the optimal trajectories of multiple unmanned aerial vehicles (UAVs) for searching target in complex environment, based on the method of Gaussian mixture model (GMM) and receding horizon control (RHC). The goal of cooperative searching problem is to obtain the maximum probability of finding the target during given flight time under various constraints, e.g., obstacle/collision avoidance and simultaneous arrival at the given destination. Hence it is taken as a complicated discrete optimization problem in this paper. First, GMM is utilized to approximate the prior known target probability distribution map, and the searching region is hence decomposed where several subregions representing a cluster of target probability can be extracted. Second, these subregions are prioritized hierarchically by evaluating their Gaussian components obtained from GMM, and then allocated to UAVs aiming to maximize the predicted mission payoff. Third, each UAV visits its allocated subregions sequentially, and the corresponding trajectory is obtained by RHC-based concurrent method. Finally, the proposed method is demonstrated and compared with other methods in the simulated scenario. The simulation results show its high efficiency to solve the cooperative searching problem.
