Contexts in source publication
Context 1
... (basically the driving direction as a compass direction in degrees), and (3) the number of headland paths (i.e., chosen based on the operating width and minimum turning radius of the vehicle). The outputs are; (1) a set of waypoints representing field tracks, and (2) a set of waypoints representing headland paths or polygons (as it is shown in Fig. ...
Context 2
... paths could be in the form of closed polygons adjacent to field outer boundaries and permanent obstacles, as it is shown in Fig. 2(b), and this type requires more computational time to provide more smoothed polygons. Alternatively, headland paths could be generated at both sides of the field tracks, as it is shown in Fig. 2(c) and this approach provides headland paths similar to what human drivers used to do when they drive in the field and this approach is three ...
Context 3
... paths could be in the form of closed polygons adjacent to field outer boundaries and permanent obstacles, as it is shown in Fig. 2(b), and this type requires more computational time to provide more smoothed polygons. Alternatively, headland paths could be generated at both sides of the field tracks, as it is shown in Fig. 2(c) and this approach provides headland paths similar to what human drivers used to do when they drive in the field and this approach is three times faster than generating headland paths as closed polygons because it does not require smoothing which is computationally exhaustive ...
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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.
