Pathfinding Algorithm Efficiency Analysis in 2D Grid

Zarembo, Imants; Kodors, Sergejs

doi:10.17770/etr2013vol2.868

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…LPA* is faster in smaller grids (64, 128, 256), but A* is faster in larger grids (512, 1024). Which leads to the conclusion, that LPA* is better suited for smaller pathfinding problems, while A* is better used to solve larger problems [9].…”

Section: Rudimentarymentioning

confidence: 99%

Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game

et al. 2018

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Section: Rudimentarymentioning

confidence: 99%

Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game

et al. 2018

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“…After having achieved this, one can then compute optimal paths following some desired metric (e.g., Manhattan or octile) within that field. Some of the above-discussed algorithms (e.g., bio-inspired networks [24]- [26]) follow this approach and some classical approaches can be used, too, for example, BFS [1], [2], Dijkstra's algorithm [3], modern variants of them [36]- [38], or reinforcement learning approaches such as TD-learning [39]. One central advantage of this is that multisource multitarget path-finding problems can be directly addressed this way.…”

Section: A State Of the Artmentioning

confidence: 99%

Finding Optimal Paths Using Networks Without Learning—Unifying Classical Approaches

Kulvičius¹,

Herzog²,

Tamošiūnaitė³

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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Trajectory or path planning is a fundamental issue in a wide variety of applications. In this article, we show that it is possible to solve path planning on a maze for multiple start point and endpoint highly efficiently with a novel configuration of multilayer networks that use only weighted pooling operations, for which no network training is needed. These networks create solutions, which are identical to those from classical algorithms such as breadth-first search (BFS), Dijkstra's algorithm, or TD(0). Different from competing approaches, very large mazes containing almost one billion nodes with dense obstacle configuration and several thousand importance-weighted path endpoints can this way be solved quickly in a single pass on parallel hardware.

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“…This freedom allows the algorithms to run to completion without interruption-making it simple to record the total time taken to compute the path. Numerous studies have used this value as an indicator of an algorithm's computational efficiency [21][22][23]. However, there are limitations to this approach, the value (typically in seconds) is specific to the graph and the hardware the algorithm was applied to.…”

Section: Computational and Memory Performancementioning

confidence: 99%

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

Pyke¹,

Stark

2021

Front. Appl. Math. Stat.

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In recent years unmanned aerial vehicles (UAVs) have become smaller, cheaper, and more efficient, enabling the use of multiple autonomous drones where previously a single, human-operated drone would have been used. This likely includes crisis response and search and rescue missions. These systems will need a method of navigating unknown and dynamic environments. Typically, this would require an incremental heuristic search algorithm, however, these algorithms become increasingly computationally and memory intensive as the environment size increases. This paper used two different Swarm Intelligence (SI) algorithms: Particle Swarm Optimisation and Reynolds flocking to propose an overall system for controlling and navigating groups of autonomous drones through unknown and dynamic environments. This paper proposes Particle Swarm Optimisation Pathfinding (PSOP): a dynamic, cooperative algorithm; and, Drone Flock Control (DFC): a modular model for controlling systems of agents, in 3D environments, such that collisions are minimised. Using the Unity game engine, a real-time application, simulation environment, and data collection apparatus were developed and the performances of DFC-controlled drones—navigating with either the PSOP algorithm or a D* Lite implementation—were compared. The simulations do not consider UAV dynamics. The drones were tasked with navigating to a given target position in environments of varying size and quantitative data on pathfinding performance, computational and memory performance, and usability were collected. Using this data, the advantages of PSO-based pathfinding were demonstrated. PSOP was shown to be more memory efficient, more successful in the creation of high quality, accurate paths, more usable and as computationally efficient as a typical incremental heuristic search algorithm when used as part of a SI-based drone control model. This study demonstrated the capabilities of SI approaches as a means of controlling multi-agent UAV systems in a simple simulation environment. Future research may look to apply the DFC model, with the PSOP algorithm, to more advanced simulations which considered environment factors like atmospheric pressure and turbulence, or to real-world UAVs in a controlled environment.

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Pathfinding Algorithm Efficiency Analysis in 2D Grid

Cited by 10 publications

References 4 publications

Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game

Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game

Finding Optimal Paths Using Networks Without Learning—Unifying Classical Approaches

Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation

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