AMRA*: Anytime Multi-Resolution Multi-Heuristic A*

Saxena, Dhruv Mauria; Kusnur, Tushar; Likhachev, Maxim

doi:10.48550/arxiv.2110.05328

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2022

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“…Du et al [13] perform multiple A* searches in parallel, each at a different resolution of the state space. This approach has been extended to an anytime algorithm by Saxena et al [14]. The idea of multiresolution has also be considered in combination with high-dimensional state lattices.…”

Section: Related Workmentioning

confidence: 99%

Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces

Sebastian¹,

Schleich²,

Behnke³

2022

Preprint

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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 contain the area around a single lower-dimensional path but consists of the union of multiple near-optimal paths. Thus, it is less prone to local minima. Furthermore, we propose an anytime algorithm using δ-Spaces of increasing sizes. We compare our method against state-of-the-art search-based trajectory planning methods and evaluate it in 2D and 3D environments to generate second-order and third-order UAV trajectories.

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Section: Related Workmentioning

confidence: 99%