A sampling-based probabilistic path planner for multirotor air vehicles in cluttered environments

Successful navigation of small, unmanned aerial vehicles (UAVs) in cluttered environments is a challenging task, especially in the presence of turbulent winds and state estimation uncertainty. This paper proposes a probabilistic path planner for UAVs operating in cluttered environments. Unlike previous sampling-based approaches which select robust paths from a set of trajectory candidates, the proposed algorithm seeks to modify an initial desired path so that it satisfies obstacle avoidance constraints. Given a desired path, Monte Carlo uncertainty propagation is performed and obstacle collision risks are quantified at discrete intervals along the trajectory. A numerical optimization algorithm is used to modify the flight path around obstacles and reduce probability of collision while maintaining as much of the originally desired path as possible. The proposed path planner is specifically designed to leverage embedded massively parallel computers for near real-time uncertainty propagation. Thus the planner can be run in real-time in a feedback manner, modifying the path appropriately as new measurements are obtained. Example results for a standard quadrotor show the ability of the path planning scheme to successfully generate trajectories in cluttered environments. Trade studies characterize algorithm performance as a function of obstacle density and collision risk acceptability.