Search-based Planning of Dynamic MAV Trajectories Using Local Multiresolution State Lattices
This work addresses the problem of efficient trajectory planning for MAVs in dynamic settings, though it is incremental as it adapts an existing spatial method to a new domain.
The paper tackled the computational expense of search-based planning for micro aerial vehicle (MAV) trajectories by applying local multiresolution to high-dimensional state lattices, resulting in significantly reduced planning times that enable frequent replanning in large dynamic environments.
Search-based methods that use motion primitives can incorporate the system's dynamics into the planning and thus generate dynamically feasible MAV trajectories that are globally optimal. However, searching high-dimensional state lattices is computationally expensive. Local multiresolution is a commonly used method to accelerate spatial path planning. While paths within the vicinity of the robot are represented at high resolution, the representation gets coarser for more distant parts. In this work, we apply the concept of local multiresolution to high-dimensional state lattices that include velocities and accelerations. Experiments show that our proposed approach significantly reduces planning times. Thus, it increases the applicability to large dynamic environments, where frequent replanning is necessary.