3-D Trajectory Planning of Aerial Vehicles Using RRT*

TLDR: This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles based on the optimal rapidly exploring random tree (RRT*) algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation.

Abstract: This brief presents a trajectory planning algorithm for aerial vehicles traveling in 3-D space while avoiding obstacles. The nature of the obstacles can be, for example, radar detection areas, cooperating and non-cooperating vehicles, and so on. Thus, it is a complex trajectory planning problem. The proposed planner is based on the optimal rapidly exploring random tree (RRT*) algorithm. Artificial potential fields are combined with the RRT* algorithm to accelerate the convergence speed to a suboptimal solution by biasing the random state generation. The performance of this framework is demonstrated on a complex missile application in a heterogeneous environment. Indeed, since the air density decreases exponentially with altitude, the maneuverability of the aerial vehicle depending on aerodynamic forces also decreases exponentially with altitude. To face this problem, the shortest paths of Dubins-like vehicles traveling in a heterogeneous environment are used to build the metric. In the simulation results, this framework can find the first solution with fewer iterations than the RRT and the RRT* algorithm. Moreover, the final solution obtained within a given number of iterations is closer to an optimal solution regarding the considered criterion.