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In this work, we propose a search-based planning method to computedynamically feasible trajectories for a quadrotor flying in anobstacle-cluttered environment. Our approach searches for smooth, minimum-timetrajectories by exploring the map using a set of short-duration motionprimitives. The primitives are generated by solving an optimal control problemand induce a finite lattice discretization on the state space which can beexplored using a graph-search algorithm. The proposed approach is able togenerate resolution-complete (i.e., optimal in the discretized space), safe,dynamically feasibility trajectories efficiently by exploiting the explicitsolution of a Linear Quadratic Minimum Time problem. It does not assume ahovering initial condition and, hence, is suitable for fast online re-planningwhile the robot is moving. Quadrotor navigation with online re-planning isdemonstrated using the proposed approach in simulation and physical experimentsand comparisons with trajectory generation based on state-of-art quadraticprogramming are presented.

Search-based Motion Planning for Quadrotors using Linear Quadratic Minimum Time Control