In this work, we propose a search-based planning method to compute dynamically feasible trajectories for a quadrotor flying in an obstacle-cluttered environment. Our approach searches for smooth, minimum-time trajectories by exploring the map using a set of short-duration motion primitives. The primitives are generated by solving an optimal control problem and induce a finite lattice discretization on the state space which can be explored using a graph-search algorithm. The proposed approach is able to generate resolution-complete (i.e., optimal in the discretized space), safe, dynamically feasibility trajectories efficiently by exploiting the explicit solution of a Linear Quadratic Minimum Time problem. It does not assume a hovering initial condition and, hence, is suitable for fast online re-planning while the robot is moving. Quadrotor navigation with online re-planning is demonstrated using the proposed approach in simulation and physical experiments and comparisons with trajectory generation based on state-of-art quadratic programming are presented.
In this work, we extend REF to account for attitude constraints by explicitly computing the robot attitude along the motion primitives based on the desired acceleration and gravity.
Search-based motion planning for quadrotors using linear quadratic minimum time control
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Computer Science Engineering