Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The navigation of mobile robots is a key element of autonomous systems, which allows robots to move effectively and securely in changing environments with greater autonomy and precision. This study aims to provide researchers with a comprehensive guide for selecting the best path-planning methods for their particular projects. We evaluate some popular algorithms that are regularly used in mobile robot navigation, in order to demonstrate their specifications and determine where they are most effective. For example, one algorithm is used to model the problem as a standard graph, and another algorithm is found to be the most suitable for highly dynamic and highly dimensional environments, due to its robust path-planning capabilities and efficient route construction. We also filter high-performance algorithms in terms of computational complexity, accuracy, and robustness. In conclusion, this study provides valuable information on its individual strengths and weaknesses, helping robotics and engineers make informed decisions when selecting the most appropriate algorithm for their specific applications.
Słowa kluczowe
Rocznik
Tom
Strony
13--22
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
autor
- AGH University of Science and Technology, Krakow, Poland
Bibliografia
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- [10] P. G. Luan, “Real-time hybrid navigation system-based path planning and obstacle avoidance for mobile robots,” Applied Sciences, 2020. [Online]. Available: doi:10.3390/app10103355
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- [13] F. Kiani, “Adapted-rrt: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms,” Neural Computing and Applications, vol. 33, pp. 15 569-15 599, 2021. [Online]. Available: doi:10.1007/s00521-021-06179-0
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- [16] G. Tang, C. Tang, C. Claramunt, X. Hu, and P. Zhou, “Geometric a-star algorithm: An improved a-star algorithm for agv path planning in a port environment,” IEEE Access, vol. 9, pp. 59 196-59 210, 2021. [Online]. Available: doi:10.1109/ACCESS.2021.3070054
- [17] N. S. Yerramilli, N. Johnson, O. S. Y. Reddy, and S. Prajwal, “Navigation systems using a*,” 2021 International Conference on Recent Trends on Electronics, Information, Communication and Technology (RTEICT), pp. 708-712, 2021. [Online]. Available: doi:10.1109/RTEICT52294.2021.9573801
- [18] Y. Li, R. Jin, X. Xu, Y. yuan Qian, H. Wang, S.-S. Xu, and Z. Wang, “A mobile robot path planning algorithm based on improved a* algorithm and dynamic window approach,” IEEE Access, vol. PP, pp. 1-1, 2022. [Online]. Available: doi:10.1109/ACCESS.2022.3179397
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- [20] J. C. Mohanta and A. Keshari, “A knowledge based fuzzy-probabilistic roadmap method for mobile robot navigation,” Applied Soft Computing Journal, vol. 79, pp. 391-409, 6 2019. [Online]. Available: doi:10.1016/J.ASOC.2019.03.055
- [21] C. Punriboon, C. So-In, P. Aimtongkham, and N. Leelathakul, “Fuzzy logic-based path planning for data gathering mobile sinks in wsns,” IEEE Access, vol. 9, pp. 96 002-96 020, 2021. [Online]. Available: doi:10.1109/ACCESS.2021.3094541
- [22] S. Aggarwal and N. Kumar, “Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges,” Computer Communications, vol. 149, pp. 270-299, 1 2020. [Online]. Available: doi:10.1016/J.COMCOM.2019.10.014
- [23] S. Wang, “Mobile robot path planning based on fuzzy logic algorithm in dynamic environment,” 2022 International Conference on Artificial Intelligence in Everything (AIE), pp. 106-110, 2022. [Online]. Available: doi:10.1109/AIE57029.2022.00027
- [24] I. Zubeiri, Y. E. Morabit, and F. Mrabti, “Genetic algorithm for vertical handover (gafvh) in a heterogeneous networks,” International Journal of Electrical and Computer Engineering, vol. 9, pp. 2534-2540, 8 2019. [Online]. Available: doi:10.11591/IJECE.V9I4.PP2534-2540
- [25] S. Katoch, S. S. Chauhan, and V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications, vol. 80, pp. 8091-8126, 2 2021. [Online]. Available: doi:10.1007/S11042-020-10139-6
- [26] M. Nazarahari, E. Khanmirza, and S. Doostie, “Multi-objective multirobot path planning in continuous environment using an enhanced genetic algorithm,” Expert Systems with Applications, vol. 115, pp. 106-120, 1 2019. [Online]. Available: doi:10.1016/J.ESWA.2018.08.008
- [27] A. N. Alfiyatin, W. F. Mahmudy, and Y. P. Anggodo, “K-means clustering and genetic algorithm to solve vehicle routing problem with time windows problem,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11, pp. 462-468, 8 2018. [Online]. Available: doi:10.11591/IJEECS.V11.I2.PP462-468
- [28] U. Orozco-Rosas, O. Montiel, and R. Sepúlveda, “Mobile robot path planning using membrane evolutionary artificial potential field,” Applied Soft Computing, vol. 77, pp. 236-251, 4 2019. [Online]. Available: doi:10.1016/J.ASOC.2019.01.036
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- [30] J. R. Sánchez-Ibáñez, C. J. Pérez-Del-pulgar, and A. García-Cerezo, “Path planning for autonomous mobile robots: A review,” Sensors, vol. 21, 12 2021. [Online]. Available: doi:10.3390/S21237898
- [31] S. Hacohen, S. Shoval, and N. Shvalb, “Probability navigation function for stochastic static environments,” International Journal of Control, Automation and Systems, vol. 17, pp. 2097-2113, 8 2019. [Online]. Available: doi:10.1007/S12555-018-0563-2
- [32] A. Faust, K. Oslund, O. Ramirez, A. Francis, L. Tapia, M. Fiser, and J. Davidson, “Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,” Proceedings - IEEE International Conference on Robotics and Automation, pp. 5113-5120, 9 2018. [Online]. Available: doi:10.1109/ICRA.2018.8461096
- [33] Z. Wang and J. Cai, “Probabilistic roadmap method for path-planning in radioactive environment of nuclear facilities,” Progress in Nuclear Energy, vol. 109, pp. 113-120, 11 2018. [Online]. Available: doi:10.1016/J.PNUCENE.2018.08.006
- [34] A. A. Ravankar, A. Ravankar, T. Emaru, and Y. Kobayashi, “Hpprm: Hybrid potential based probabilistic roadmap algorithm for improved dynamic path planning of mobile robots,” IEEE Access, vol. 8, pp. 221 743-221 766, 2020. [Online]. Available: doi:10.1109/ACCESS.2020.3043333
- [35] W. Khaksar, K. S. B. M. Sahari, F. B. Ismail, M. Yousefi, and M. A. Ali, “Runtime reduction in optimal multi-query sampling-based motion planning,” 2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014, pp. 52-56, 10 2015. [Online]. Available: doi:10.1109/ROMA.2014.7295861
- [36] L. Chen, Y. Shan, W. Tian, B. Li, and D. Cao, “A fast and efficient double-tree rrt star-like sampling-based planner applying on mobile robotic systems,” IEEE/ASME Transactions on Mechatronics, vol. 23, pp. 2568-2578, 12 2018. [Online]. Available: doi:10.1109/TMECH. 2018.2821767
- [37] J. Denny, R. Sandstr¨om, A. Bregger, and N. M. Amato, “Dynamic region-biased rapidly-exploring random trees,” Springer Proceedings in Advanced Robotics, vol. 13, pp. 640-655, 2020. [Online]. Available: doi:10.1007/978-3-030-43089-4_41
- [38] Z. Tahir, A. H. Qureshi, Y. Ayaz, and R. Nawaz, “Potentially guided bidirectionalized rrt* for fast optimal path planning in cluttered environments,” Robotics and Autonomous Systems, vol. 108, pp. 13-27, 10 2018. [Online]. Available: doi:10.1016/J.ROBOT.2018.06.013
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b0d7ab2-eaeb-4f06-a32c-b3b3b68fa63b