Three-dimensional UAV track planning based on the GB-PQ-RRT* algorithm

Progressive optimal fast search random tree algorithm (Rapidly exploring Random Tree star, RRT *) has powerful random search ability, therefore, it is widely used in the navigation track planning. However, the proposed algorithm still has the problems of low node utilization rate, slow convergence speed and high generation path cost, in order to make up for these deficiencies, a new algorithm of target bias, gravitational potential field and fast RRT
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fusion (Goal Biased-Potential Quick-RRT*, GB- PQ-RRT*) is proposed. The target bias is introduced to optimize the selection method of the sampling point, so that the search tree grows in the direction of the target point with a certain probability. The gravitational potential field is added to optimize the generation mode of new nodes and speed up the convergence speed of the algorithm; The Q-RRT* algorithm was then combined to optimize the path. The simulation results show that the new algorithm generates shorter, smoother paths and consumes less time in 3D environments.