This paper presents an efficient and feasible algorithm for the path planning problem of the multiple unmanned aerial vehicles (multi-UAVs) formation in a known and realistic environment. The artificial potential field method updated by the additional control force is used for establishing two models for the single UAV, which are the particle dynamic model and the path planning optimization model. The additional control force can be calculated by using the optimal control method. Furthermore, the multi-UAV path planning model is established by introducing "virtual velocity rigid body" and "virtual target point". Then, the motion states of the lead plane and wingmen are obtained from the path planning model. Finally, the path following process based on the quadrotor helicopter PID controllers is introduced to verify the rationality of the path planning results. The simulation results show that the artificial potential method with the additional control force improved by the optimal control method has a good path planning ability for the single UAV and the all UAVs formation. At the same time, the path planning results are available and the UAVs can basically track the UAV formation.
This paper deals with a UAV path planning problem in the environment where both solid obstacles and soft obstacles exist. The artificial potential field approach is updated by introducing an additional control force and integrating it with the concept of receding horizon control for UAV trajectory optimization. The original problem is converted into a multi-objective optimization problem by regarding the involved additional control term as the optimization variable. Seeing as the establishment of an additional control force of soft obstacles is dependent on the probability of certain specifications such as survivability, the additional control term accomplishes the description of the specified property index better than those that have been considered in the past, such as distance and control energy. The optimal solution to the path planning problem, in terms of the additional control force method, and the computational efficiency according to the receding horizon control, both contribute to the proposed algorithm. Meanwhile, the proposed method is able to protect the UAV from local minima of additional control force. The numerical examples verify the advantages of this approach.
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