A probabilistic B-Spline motion planning algorithm for unmanned helicopters flying in dense 3D environments

This paper presents a strategy for improving motion planning of an unmanned helicopter flying in a dense and complex city-like environment. Although Sampling Based Motion planning algorithms have shown success in many robotic problems, problems that exhibit "narrow passage" properties involving kinodynamic planning of high dimensional vehicles like aerial vehicles still present computational challenges. In this work, to solve the kinodynamic motion planning problem of an unmanned helicopter, we suggest a two step planner. In the first step, the planner explores the environment through a randomized reachability tree search using an approximate line segment model. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In the second step, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a dynamically feasible path. Numerical simulations in 3D indicate the ability of the method to provide real-time solutions in dense and complex environments.