Search-based Motion Planning for Aggressive Flight in SE(3)
This work addresses motion planning for aggressive drone flight in complex settings, representing an incremental improvement in domain-specific robotics.
The paper tackles the problem of planning aggressive flight trajectories for quadrotors in cluttered environments by developing a search-based approach that uses motion primitives and an ellipsoid collision model, enabling safe and optimal navigation through narrow gaps without prior information.
Quadrotors with large thrust-to-weight ratios are able to track aggressive trajectories with sharp turns and high accelerations. In this work, we develop a search-based trajectory planning approach that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments. We model the quadrotor body as an ellipsoid and compute its flight attitude along trajectories in order to check for collisions against obstacles. The ellipsoid model allows the quadrotor to pass through gaps that are smaller than its diameter with non-zero pitch or roll angles. Without any prior information about the location of gaps and associated attitude constraints, our algorithm is able to find a safe and optimal trajectory that guides the robot to its goal as fast as possible. To accelerate planning, we first perform a lower dimensional search and use it as a heuristic to guide the generation of a final dynamically feasible trajectory. We analyze critical discretization parameters of motion primitive planning and demonstrate the feasibility of the generated trajectories in various simulations and real-world experiments.