Dynamically Feasible Probabilistic Motion Planning in Complex Environments for UAVs

In this work, we consider the problem of generating practically implementable path plan for flying unmanned aerial vehicles in 3D Complex environments. This problem is complicated by the fact that, generation of the dynamically and geometrically feasible flight trajectories for agile maneuver profiles requires search of nonlinear state space of the aircraft dynamics. This work suggests a two step feasible trajectory generating approach. 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. After this first step we explain two different methods to create Dynamically Feasible Path, first one that we called Modal-Maneuver Based PRM Planner is suitable for agile unmanned aerial vehicles that their maneuvers can be define with distinct modes. This allows significant decreases in control input space and thus search dimensions, resulting in a natural way to design controllers and implement trajectory planning using the closed-form flight modes. In this approach the resulting connectivity path and the corresponding milestones are refined with a single query Probabilistic Road Map (PRM) implementation that creates dynamically feasible flight paths with distinct flight mode selections and their modal control inputs. In our second approach that we called Probabilistic B-Spline Planner, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a geometrically and dynamically feasible path. This generated feasible path is turned in to time depended trajectory with using time scale factor considering the velocity and acceleration limits of the aircrafts.