A Probabilistic Algorithm for Mode Based Motion Planning of Agile Unmanned Air Vehicles in Complex Environments

Abstract

Abstract:In this work, we consider the design of a probabilistic trajectory planner for a highly maneuverable unmanned air vehicle flying in a dense and complex city-like environment. Our design hinges on the decomposition of the problem into a) flight controls of fundamental agile-maneuvering flight modes and b) trajectory planning using these controlled flight modes from which almost any aggressive maneuver (or a combination of) can be created. 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. Focusing on the trajectory planning part, we provide a three-step probabilistic trajectory planner. In the first step, the algorithm rapidly explores the environment through a randomized reachability tree search using an approximate line segment models. The resulting connecting path is converted into flight milestones through a line-of-sight segmentation. This 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. We address the problematic issue of narrow passages through non-uniform distributed capture regions, which prefer state solutions that align the vehicle to enter the milestone region in line with the next milestone to come. Numerical simulations in 3D and 2D demonstrate the ability of the method to provide real-time solutions in dense and complex environments.

Late Line-of-Sight Check and Prioritized Trees for Path Planning

“…LoS-Check helps the planners generate better solutions at a faster speed, but it could still be improved. There are also many other path optimization methods similar to LoS-Check [40][41][42].…”

Flight deck automation support with dynamic 4D trajectory management for ACAS: AUTOFLY-Aid

“…The algorithm envisioned is to use the TCAS solution as the initial solution and further improve on it. In our earlier work [17][18][19][20], we observed that before the major feasible path planning phase, defining the geometrical obstacle free path and tractable way points significantly accelerates the searching ability and decreases the total computational time of planner. This approach is considered to be implemented, is into two layers.…”