A new modification to the A* path-finding algorithm to improve its space and time performance
Vol 2, Issue 3, 2024
Issue release: 30 September 2024
VIEWS - 1076 (Abstract)
Download PDF
Abstract
We propose a new modification to the A* algorithm named AA* that significantly improves space and time complexities. In AA*’s forward pass, the node sets (open and closed) are not used, and only the local node neighborhood is saved to take the next move decision. AA* needs a backward pass to bridge and correct gaps and bad decisions made in the forward pass. The work of the backward pass is far less than that of the forward pass, as most of the task has been done. It is shown via empirical experimental work that our proposed AA* algorithm is superior to the classical A* algorithm in the typical three metrics: running time, number of probed nodes, and length of path. Furthermore, our experimental work showed that AA* is suboptimal in terms of length of path compared to the original Dijkstra’s algorithm with an accuracy of 96.95%.
Keywords
References
1. Rababaah H, Shirkhodaie A. Guard Duty Alarming Technique (GDAT): A Novel Scheduling Approach for Target-tracking in Large-scale Distributed Sensor Networks. In: Proceedings of 2007 IEEE International Conference on System of Systems Engineering; 16–18 April 2007; San Antonio, USA. pp. 1–6.
2. Rababaah AR. Sensor networks simulation framework for target tracking applications: SN-SiFTTA. International Journal of Web Engineering and Technology. 2021; 16(2): 113. doi: 10.1504/ijwet.2021.117767
3. Dijkstra EW. A note on two problems in connexion with graphs. Numerische Mathematik. 1959; 1(1): 269–271. doi: 10.1007/bf01386390
4. Afakh ML, Masudi MI, Ardilla F, et al. Bicycle Path Planning on Omnidirectional Mobile Robot Using Fuzzy Logic Controller. In: Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE); Bali, Indonesia. pp. 237–241.
5. Ambeskar A, Turkar V, Bondre A, et al. Path finding robot using image processing. In: Proceedings of 2016 International Conference on Inventive Computation Technologies (ICICT). pp. 1–6.
6. Chen J, Ye F, Jiang T. Path planning under obstacle-avoidance constraints based on ant colony optimization algorithm. In: Proceedings of 2017 IEEE 17th International Conference on Communication Technology (ICCT); 27–30 October 2017; Chengdu, China. pp. 1434–1438.
7. Ju C, Luo Q, Yan X. Path Planning Using an Improved A-star Algorithm. In: Proceedings of 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan); 23–25 October 2020; Jinan, China. pp. 23–26.
8. Ma N, Cao Y, Wang X, et al. A Fast path re-planning method for UAV based on improved A* algorithm. In: Proceedings of 2020 3rd International Conference on Unmanned Systems (ICUS); Harbin, China. pp. 462–467.
9. Qiao S, Zheng K, Wang G. A Path Planning Method for Autonomous Ships Based on SVM. In: Proceedings of 2020 Chinese Control and Decision Conference (CCDC); 22–24 August 2020; Hefei, China. pp. 3068–3072.
10. Rababaah A, Kuscu E, Shirkhodaie A. Indoor Mobile Robot Localization Using IPS Cricket Technology. Journal of Global Information Technology (JGIT). 2014; 9(1): 18–23.
11. Shen Z, Hao Y, Li K. Application research of an Adaptive Genetic Algorithms based on information entropy in path planning. In: Proceedings of the 2010 IEEE International Conference on Information and Automation; 20–23 June 2010; Harbin, China. pp. 2013–2016.
12. Szczepanski R, Tarczewski T, Erwinski K. Energy Efficient Local Path Planning Algorithm Based on Predictive Artificial Potential Field. IEEE Access. 2022; 10: 39729–39742. doi: 10.1109/access.2022.3166632
13. Tusi Y, Chung HY. Using ABC and RRT algorithms to improve mobile robot path planning with danger degree. In: Proceedings of 2016 Fifth International Conference on Future Generation Communication Technologies (FGCT); 17–19 August 2016; London, UK. pp. 21–26.
14. Yang R, Cheng L. Path Planning of Restaurant Service Robot Based on A-star Algorithms with Updated Weights. In: Proceedings of 2019 12th International Symposium on Computational Intelligence and Design (ISCID); Hangzhou, China. pp. 292–295.
15. Zhu Z, Li L, Wu W, et al. Application of improved Dijkstra algorithm in intelligent ship path planning. In: Proceedings of 2021 33rd Chinese Control and Decision Conference (CCDC); 22–24 May 2021; Kunming, China. pp. 4926–4931.
16. Hart P, Nilsson N, Raphael B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics. 1968; 4(2): 100–107. doi: 10.1109/tssc.1968.300136.
17. Kuang H, Li Y, Zhang Y, et al. Improved A-star Algorithm based on Topological Maps for Indoor Mobile Robot Path Planning. In: Proceedings of 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC); 4–6 March 2022; Chongqing, China. pp. 1236–1240.
18. Sun T, Wang T, Sun P. Mobile Robot Dynamic Path Planning Based on Improved A* Algorithm. In: Proceedings of 2021 3rd International Conference on Robotics and Computer Vision (ICRCV); Beijing, China. pp. 24–29.
19. Şahın Hİ, Kavsaoğlu AR. Indoor Path Finding and Simulation for Smart Wheelchairs. In: Proceedings of 2021 29th Signal Processing and Communications Applications Conference (SIU); Istanbul, Turkey. pp. 1–4.
20. Hu Z, Li J. Application of Maintaining the Shortest Path Method in the Game Map Path-Finding. In: Proceedings of 2010 International Conference on Computational Aspects of Social Networks; Taiyuan, China. pp. 737–740.
21. Kim OTT, Nguyen VD, Moon S, Hong CS. Finding realistic shortest path in road networks with lane changing and turn restriction. In: Proceedings of the18th Asia-Pacific Network Operations and Management Symposium (APNOMS), 2016. pp.1–4.
22. Zuo G, Zhang P, Qiao J. Path planning algorithm based on sub-region for agricultural robot. In: Proceedings of the 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). pp. 197–200.
23. Wang H, Qi X, Lou S, et al. An Efficient and Robust Improved A* Algorithm for Path Planning. Symmetry. 2021; 13(11): 2213. doi: 10.3390/sym13112213
24. Johsonbaugh R. Discrete Mathematics, 8th ed. Pearson; 2018.
25. Matlab. 9.4.0.813654 (R2018a). USA: The MathWorks Inc; 2018.
26. Shirkhodaie A, Rababaah H. “Multi-layered context impact modulation for enhanced focus of attention of situational awareness in persistent surveillance systems”, Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications. 2010; 771009. doi: 10.1117/12.850795.
27. Rababaah A, As’ad A, Sultan A, et al. Development of Robotic Model to Support Intelligent Vehicles Behaviors. Global Journal of Modeling and Computational Intelligence (GJMCI). 2021; 1(1): 50–59.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Aaron Rasheed Rababaah
License URL: https://creativecommons.org/licenses/by/4.0/
Prof. Maode Ma
Qatar University, Qatar
Processing Speed (2023)
-
-
-
-
-
- <7 days: submission to screening review decision
- 48 days: received to accepted
- 59 days: received to online
-
-
-
-
The field of computer and telecommunications engineering is rapidly advancing, with the following being some of the latest developments.
more
We are pleased to congratulate the first anniversiry of the journal of Computer and Telecommunication Engineering (CTE).
more
Owing to the tireless dedication of the editor-in-chief, editorial board members, and the in-house editorial team, we are proud to announce the successful online launch of the first issue of Computer and Telecommunication Engineering.
Please follow the journal's author guidelines and the new article template to prepare your manuscript.
Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.