Expert Systems with Applications

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree. • The extension of the RRT* can reduce the convergence time. • The implementation of improved APF to decrease redundancy points. • Combination with goal-biased strategy to obtain higher quality sampling. • Generating smoother path optimized by cubic spline to lessen vertexes.