Obtaining the optimal cost-to-go map for large scale rough terrains is computationally very expensive both in terms of duration and memory resources. A fast algorithm for approximation of the optimal cost-to-go map in terms of terrain traversability measures for path planning on known large scale rough terrains is developed. The results show that the majority of the cost-to-go map values, computed from every terrain location with respect to the goal location, are near-optimal. Unlike Dijkstra algorithm, the proposed algorithm has inherently parallel structure, and can be significantly speeded up depending on the number of used CPU cores.

A fast cost-to-go map approximation algorithm on known large scale rough terrains

MAGNANI, GIANANTONIO
2015

Abstract

Obtaining the optimal cost-to-go map for large scale rough terrains is computationally very expensive both in terms of duration and memory resources. A fast algorithm for approximation of the optimal cost-to-go map in terms of terrain traversability measures for path planning on known large scale rough terrains is developed. The results show that the majority of the cost-to-go map values, computed from every terrain location with respect to the goal location, are near-optimal. Unlike Dijkstra algorithm, the proposed algorithm has inherently parallel structure, and can be significantly speeded up depending on the number of used CPU cores.
Proceeding of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/973117
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