Optimized A-Star algorithm in hexagon-based environment using parallel bidirectional search

Saian, Pratyaksa Ocsa Nugraha; Suyoto, '; Pranowo, Pranowo

doi:10.1109/iciteed.2016.7863246

Cited by 17 publications

(6 citation statements)

References 14 publications

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“…The A-Star algorithm is one of the best-known path planning algorithms [16], which can be applied to a configuration space metric or topology. This algorithm uses heuristic search and search based on the shortest path [17]. In terms of search, this algorithm is good in various environments [4].…”

Section: A-star Algorithmmentioning

confidence: 99%

“…The A-Star algorithm guides the optimal path to the goal if the heuristic function h(n) is acceptable, meaning that it will never overestimates the original cost [19] or actual cost [20]. Evaluation function f(n) = g(n) + h(n), where [17][21] [22]: g(n) = cost so far to reach n. h(n) = estimated cost from n to target (goal). f(n) = estimated total cost of the path through n to the target.…”

Section: A-star Algorithmmentioning

confidence: 99%

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Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

Wayahdi

Ginting

Syahputra

2021

Int. J. Adv. Data Inf. Syst.

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The problem of finding the shortest path from a path or graph has been quite widely discussed. There are also many algorithms that are the solution to this problem. The purpose of this study is to analyze the Greedy, A-Star, and Dijkstra algorithms in the process of finding the shortest path. The author wants to compare the effectiveness of the three algorithms in the process of finding the shortest path in a path or graph. From the results of the research conducted, the author can conclude that the Greedy, A-Star, and Dijkstra algorithms can be a solution in determining the shortest path in a path or graph with different results. The Greedy algorithm is fast in finding solutions but tends not to find the optimal solution. While the A-Star algorithm tends to be better than the Greedy algorithm, but the path or graph must have complex data. Meanwhile, Dijkstra's algorithm in this case is better than the other two algorithms because it always gets optimal results.

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Section: A-star Algorithmmentioning

confidence: 99%

Section: A-star Algorithmmentioning

confidence: 99%

Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

Wayahdi

Ginting

Syahputra

2021

Int. J. Adv. Data Inf. Syst.

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“…Reference [10] proposes an improved A* algorithm which takes the grid boundary instead of the grid centre as nodes, to make the planned paths smoother. Reference [11] uses two different directions of hexagonal grids to re-rasterise the environment, and at the same time uses the computer's dual core to design parallel algorithm in the hexagonal environment, which improves the efficiency of A* operation.…”

Section: Introductionmentioning

confidence: 99%

Real time path planning via alternating minimisation through image information

Chen

Zhang

Zhu

et al. 2021

IET Cyber-Syst and Robotics

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Real time path planning from image information is of vital importance in the fields of robots as it has various applications in real time navigation, autonomous driving, robot arm manipulation and human robot cooperation and so on. To achieve this task, a two stage approach is proposed. At the first stage, a novel change point detection approach is proposed to process the image to extract the shape of the obstacles. At the second stage, several novel approximations are adopted to make the path planning problem tractable. Firstly, the irregular shapes of the obstacles in the environment are approximated as lines and circles, which simplify the distance constraint significantly. Secondly, the non-convex path planning problem is iteratively decomposed as a sequence of subproblems and alternating minimisation method is proposed to efficiently solve the subproblem. To improve the quality of the solution, good initial points obtained by A* algorithm is provided. Both numerical experiments and real experiments are conducted to demonstrate the effectiveness of the proposed algorithm.

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“…All planners in this category are based on a cell decomposition which uses a search algorithm to find the collision-free path. The most used search algorithms are Dijkstra's, A*, the local current comparison, and any of its variants [22][23][24][25][26][27][28].…”

mentioning

confidence: 99%

“…One of the main contributions in the present work is the implementation of a systematic criterion to select the repulsion parameter to generate an optimal path, in terms of length and execution time, for HPPM. [32] Single Probabilistically Sampling-based Collision query RRT* [7] Single Probabilistically Sampling-based Collision query PRM [7,29] Multiple Probabilistically Sampling-based Collision query EST [30] Single Probabilistically Sampling-based Collision query KPIECE [1] Single Probabilistically Sampling-based Collision query LazyRRT [33] Single Probabilistically Sampling-based Density of nodes LazyPRM [34] Multiple Probabilistically Sampling-based Density of nodes APF [16][17][18] Single Semi-complete Potential fields Local minima A* [24][25][26] Single Resolution Cells decomposition Grid resolution Visibility graphs [12][13][14][15] Multiple Complete Graph-based Obstacles density HPPM [10,19,20] Single Semi-complete HCM (Mathematical model) Repulsion parameter selection…”

mentioning

confidence: 99%

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

Diaz-Arango

Vázquez-Leal

Hernandez-Martinez

et al. 2020

Sensors

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The ability to plan a multiple-target path that goes through places considered important is desirable for autonomous mobile robots that perform tasks in industrial environments. This characteristic is necessary for inspection robots that monitor the critical conditions of sectors in thermal, nuclear, and hydropower plants. This ability is also useful for applications such as service at home, victim rescue, museum guidance, land mine detection, and so forth. Multiple-target collision-free path planning is a topic that has not been very studied because of the complexity that it implies. Usually, this issue is left in second place because, commonly, it is solved by segmentation using the point-to-point strategy. Nevertheless, this approach exhibits a poor performance, in terms of path length, due to unnecessary turnings and redundant segments present in the found path. In this paper, a multiple-target method based on homotopy continuation capable to calculate a collision-free path in a single execution for complex environments is presented. This method exhibits a better performance, both in speed and efficiency, and robustness compared to the original Homotopic Path Planning Method (HPPM). Among the new schemes that improve their performance are the Double Spherical Tracking (DST), the dummy obstacle scheme, and a systematic criterion to a selection of repulsion parameter. The case studies show its effectiveness to find a solution path for office-like environments in just a few milliseconds, even if they have narrow corridors and hundreds of obstacles. Additionally, a comparison between the proposed method and sampling-based planning algorithms (SBP) with the best performance is presented. Furthermore, the results of case studies show that the proposed method exhibits a better performance than SBP algorithms for execution time, memory, and in some cases path length metrics. Finally, to validate the feasibility of the paths calculated by the proposed planner; two simulations using the pure-pursuit controlled and differential drive robot model contained in the Robotics System Toolbox of MATLAB are presented.

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Optimized A-Star algorithm in hexagon-based environment using parallel bidirectional search

Cited by 17 publications

References 14 publications

Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

Greedy, A-Star, and Dijkstra’s Algorithms in Finding Shortest Path

Real time path planning via alternating minimisation through image information

Multiple-Target Homotopic Quasi-Complete Path Planning Method for Mobile Robot Using a Piecewise Linear Approach

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