Parallel implementation of A* search algorithm for road network

Belhaous, Safa; Baroud, Sohaib; Chokri, Soumia; Hidila, Zineb; Naji, Abdelwahab; Mestari, Mohammed

doi:10.1109/icds47004.2019.8942279

Cited by 4 publications

(1 citation statement)

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“…where g(n) is the actual cost from node n to the initial node, and h(n) is the cost of the optimal path from the target node to n. The A* algorithm is widely used in the gaming industry [26], and with the development of artificial intelligence, the A* algorithm has since been improved and tailored for applications, including robot path planning, urban intelligent transportation, graph theory, and automatic control [27][28][29][30][31].…”

Section: A* Algorithmmentioning

confidence: 99%

A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

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