Abstract:UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal, however. On the other hand, direct planning in higher-dimensional state spaces generates globally optimal solutions but is time-consuming and thus infeasible for timeconstrained applications. To address this issue, we propose δ-Spaces, a pruned high-dimensional state space representation for trajectory refinement. It does not only co… Show more
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