Unmanned aerial vehicles (UAVs) are widely used in search problem due to their portability and high performance. In UAV-assisted search problem, the path planning is considered as the coverage path planning problem, which is usually converted to a traveler's problem through the grid decomposition method. To solve this problem, this paper has designed an improved ant colony algorithm, which combines Q-learning based adaptive strategy, elite strategy and other methods to enhance the exploration and convergence ability. Simulation results show that the method can effectively improve the coverage efficiency of the multi-UAV multi-area coverage search problem and reduce the UAV flight energy consumption.
In this work, the method of Model Predictive Control (MPC) and Improved Whale Optimization Algorithm (IWOA) has been proposed to solve multiple unmanned aerial vehicles (UAVs) tracking a moving target in urban environment. The problem models are established, including the UAV model, target model, environment model and cost function model. Adopting MPC as a control framework for UAV target tracking, WOA is chosen as the solver of MPC. To further improve the optimized efficiency, the introduced strategies include bootstrap initialization strategy, double-difference variational strategy, adaptive weighting strategy and elite selection strategy. The compared experiments show the control method in this paper has better tracking performance and is a reliable technique for UAV tracking the moving target.
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