Planning Dynamically Feasible Trajectories for Quadrotors Using Safe Flight Corridors in 3-D Complex Environments

TLDR

Extensive literature uses convex optimization to generate piece‑wise polynomial trajectories for differential‑flat micro‑aerial vehicles, and the Safe Flight Corridor (SFC) is a collection of convex overlapping polyhedra that models free space and provides a connected path from the robot to the goal. The study proposes formulating trajectory generation as a quadratic program using a Safe Flight Corridor. The authors build the SFC via an efficient convex decomposition of a piece‑wise linear skeleton from a fast graph search, use it to impose linear inequality constraints in a QP for real‑time planning, and employ a receding‑horizon framework that replans trajectories within a limited sensor footprint, taking 50–300 ms per update in large, cluttered maps. The approach is feasible, complete, and performs well, enabling high‑speed quadrotor flight in both simulation and real‑world experiments.

Abstract

There is extensive literature on using convex optimization to derive piece-wise polynomial trajectories for controlling differential flat systems with applications to three-dimensional flight for Micro Aerial Vehicles. In this work, we propose a method to formulate trajectory generation as a quadratic program (QP) using the concept of a Safe Flight Corridor (SFC). The SFC is a collection of convex overlapping polyhedra that models free space and provides a connected path from the robot to the goal position. We derive an efficient convex decomposition method that builds the SFC from a piece-wise linear skeleton obtained using a fast graph search technique. The SFC provides a set of linear inequality constraints in the QP allowing real-time motion planning. Because the range and field of view of the robot's sensors are limited, we develop a framework of Receding Horizon Planning , which plans trajectories within a finite footprint in the local map, continuously updating the trajectory through a re-planning process. The re-planning process takes between 50 to 300 ms for a large and cluttered map. We show the feasibility of our approach, its completeness and performance, with applications to high-speed flight in both simulated and physical experiments using quadrotors.