UAV trajectory planning based on APF-RRT* algorithm with goal-biased strategy

In recent decades, Rapidly-exploring random tree star(RRT*) algorithm has attracted much attention because of its asymptotic optimization properties. However, it has slow convergence rate and large randomness in search range. Aiming at the shortcomings of this algorithm, unmanned aerial vehicle(UAV) trajectory planning method based on goalbiased artificial potential field(APF)-RRT* algorithm is proposed. Firstly, the goal-biased strategy is used to guide the generation of random sampling points to accelerate the convergence rate of the algorithm. Secondly, the improved artificial potential field method is introduced into the random search tree, which greatly reduces the number of iterations. The combination of the two algorithms generates new nodes with higher quality and decreases greatly path cost. Finally, the search performance of growing trees is enhanced greatly by comparing the algorithm with RRT* and Informed-RRT*.