UAV trajectory planning in cluttered environments based on PF-RRT* algorithm with goal-biased strategy

Abstract

In recent decades, Rapidly-exploring Random Tree star(RRT*) with asymptotic optimality has attracted much attention in path planning algorithm, but it suffers from slow convergence. Hence to solve the drawback, this paper proposes a novel Unmanned Aerial Vehicle(UAV) trajectory planning in cluttered environments based on PF-RRT* algorithm with goal-biased strategy. It creates a novel parent node for the new node near the obstacle by dichotomy method, instead of updating the parent node in the existing random tree nodes, which considerably decreases the path cost. The improved artificial potential field(APF) is proposed to guide the growth of the random tree towards the target point by adding random point attraction, target point attraction and obstacle repulsion, which not only addresses the local minimum problem, but also boosts the search rate of the random tree. The algorithm proposed in this paper combines with goal-biased strategy to obtain higher quality sampling points during the sampling process. Finally, the simulation verifies that the proposed algorithm is greatly optimized in terms of the number of iterations, convergence rate and path cost.