Вход на сайт

Просмотр новости

Найдите то, что Вас интересует

Reduction of Path Length of Notable Path Algorithms Using the KNS Algorithm

Дата публикации: 12-04-2026 22:00:00

Publication date: 13 April 2026
Source: Applied Mechanics and Materials Vol. 935
Author(s): Kenneth Christopher Ugwoke, Nnanna Nwojo Agwu, Saleh El-Yakubu Abdullahi
Path planning refers to designing a reliable, feasible, optimum, safe, and collision-free path with the shortest distance that takes a mobile robot from the start position to the goal point within an environment. To ensure the successful operation of a robot, an effective and efficient Path planning technique that guarantees obstacle avoidance and an optimal path must be adopted. This paper applies a novel path length reduction technique – the Kenneth, Nnanna, and Saleh (KNS) algorithm-to notable path planning algorithms (APF, A*, RRT, and RRT*) to shorten their path length by reducing the waypoints' bends and retaining the obstacle avoidance capability of the algorithms. We simulated applying the technique to different notable algorithms in an environment configured with varying obstacles. We compared the resultant paths with the original paths. The results show that the KNS algorithm is very effective and can significantly reduce the path length of the notable algorithms.


Основное содержимое страницы с новостью.

[1] H. Qin, S. Shao, T. Wang, X. Yu, Y. Jiang, and Z. Cao, "Review of Autonomous Path Planning Algorithms for Mobile Robots," Drones, vol. 7, no. 3, 2023.

DOI: 10.3390/drones7030211

Google Scholar

[2] L. Liu, X. Wang, X. Yang, H. Liu, J. Li, and P. Wang, "Path planning techniques for mobile robots: Review and prospect," Expert Syst Appl, vol. 227, no. April, p.120254, Oct. 2023.

DOI: 10.1016/j.eswa.2023.120254

Google Scholar

[3] S. Dian, J. Zhong, B. Guo, J. Liu, and R. Guo, "A smooth path planning method for mobile robot using a BES-incorporated modified QPSO algorithm," Expert Syst Appl, vol. 208, p.118256, 2022.

DOI: 10.1016/j.eswa.2022.118256

Google Scholar

[4] A. Gharbi, "A dynamic reward-enhanced Q-learning approach for efficient path planning and obstacle avoidance in mobile robotics," Applied Computing and Informatics, 2024.

DOI: 10.1108/ACI-10-2023-0089

Google Scholar

[5] M. M. Costa and M. F. Silva, "A Survey on Path Planning Algorithms for Mobile Robots," 19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019, p.448–468, 2019.

DOI: 10.1109/ICARSC.2019.8733623

Google Scholar

[6] M. M. Costa and M. F. Silva, "A Survey on Path Planning Algorithms for Mobile Robots," 19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019, p.448–468, 2019.

DOI: 10.1109/ICARSC.2019.8733623

Google Scholar

[7] X. Li, G. Li, and Z. Bian, "Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method," Sensors, vol. 24, no. 12, p.3899, Jun. 2024.

DOI: 10.3390/s24123899

Google Scholar

[8] R. Raj and A. Kos, "A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives," Applied Sciences (Switzerland), vol. 12, no. 14, 2022.

DOI: 10.3390/app12146951

Google Scholar

[9] C. Ren, F. Fu, C. Yin, Z. Yan, R. Zhang, and Z. Wang, "Improved artificial potential field method based on robot local path information," Int J Adv Robot Syst, vol. 21, no. 5, p.1–16, Sep. 2024.

DOI: 10.1177/17298806241278172

Google Scholar

[10] K. Almazrouei, I. Kamel, and T. Rabie, "Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning," Applied Sciences (Switzerland), vol. 13, no. 14, 2023.

DOI: 10.3390/app13148174

Google Scholar

[11] C. Ren, F. Fu, C. Yin, Z. Yan, R. Zhang, and Z. Wang, "Improved artificial potential field method based on robot local path information," Int J Adv Robot Syst, vol. 21, no. 5, p.1–16, Sep. 2024.

DOI: 10.1177/17298806241278172

Google Scholar

[12] A. A. Nippun Kumaar and S. Kochuvila, "Mobile Service Robot Path Planning Using Deep Reinforcement Learning," IEEE Access, vol. 11, no. September, p.100083–100096, 2023.

DOI: 10.1109/ACCESS.2023.3311519

Google Scholar

[13] J. Gao, W. Ye, J. Guo, and Z. Li, "Deep reinforcement learning for indoor mobile robot path planning," Sensors (Switzerland), vol. 20, no. 19, p.1–15, 2020.

DOI: 10.3390/s20195493

Google Scholar

[14] Y. Tang, M. A. Zakaria, and M. Younas, "Path Planning Trends for Autonomous Mobile Robot Navigation: A Review," Sensors, vol. 25, no. 4, p.1206, Feb. 2025.

DOI: 10.3390/s25041206

Google Scholar

[15] S. Dawnee, M. M. S. Kumar, S. Jayanth, and V. K. Singh, "Experimental performance evaluation of various path planning algorithms for obstacle avoidance in UAVs," Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, p.1029–1034, 2019.

DOI: 10.1109/ICECA.2019.8821841

Google Scholar

[16] H. Sang, Y. You, X. Sun, Y. Zhou, and F. Liu, "The hybrid path planning algorithm based on improved A* and artificial potential field for unmanned surface vehicle formations," Ocean Engineering, vol. 223, no. January, p.108709, 2021, doi:

DOI: 10.1016/j.oceaneng.2021.108709

Google Scholar

[17] S. M. H. Rostami, A. K. Sangaiah, J. Wang, and X. Liu, "Obstacle avoidance of mobile robots using modified artificial potential field algorithm," EURASIP J Wirel Commun Netw, vol. 2019, no. 1, 2019.

DOI: 10.1186/s13638-019-1396-2

Google Scholar

[18] A. N. A. Rafai, N. Adzhar, and N. I. Jaini, "A Review on Path Planning and Obstacle Avoidance Algorithms for Autonomous Mobile Robots," Journal of Robotics, vol. 2022, p.1–14, Dec. 2022.

DOI: 10.1155/2022/2538220

Google Scholar

[19] L. Yang, Path Planning Technique for Mobile Robots : A Review. 2023.

Google Scholar

[20] Y. B. E. N. JMAA and D. DUVIVIER, "A Review of Path Planning Algorithms BT  - Intelligent Systems Design and Applications," A. Abraham, A. Bajaj, T. Hanne, P. Siarry, and K. Ma, Eds., Cham: Springer Nature Switzerland, 2024, p.119–130.

DOI: 10.1007/978-3-031-64850-2_11

Google Scholar

Схожие новости

#Наименование новостиТональностьИнформативностьДата публикации
1Top-Level Structural and Mechatronic Design of a 6WD Outdoor Autonomous Delivery Robot with a Redesigned Adaptive Climbing Rocker-Bogie Suspension010.7612-04-2026
2Adaptive Neural Network-Based Feedforward-Feedback Controller for Nonlinear Dynamic System013.5312-04-2026
3Development and Simulation of an Algorithm for UAV Swarm Coordination and Collision Avoidance010.8412-04-2026
4A Systematic Review on Bioinspired Robotic Grippers and Manipulators09.3812-04-2026
5An AI-Powered Control System For Robots With Legs025.3806-07-2026
6Toward a ML Framework for Multisensory Human Health and Awareness011.9212-04-2026
7Российские учёные научили роботов находить кратчайший путь в сто раз быстрее08.4623-07-2026
8Future-aware AI doubles robot speed in pick-and-place tasks014.2828-07-2026
9Запатентован эффективный способ прохождения робота по трубопроводу0012-03-2025

Классификация: Наука. Схожих патентов: 0. Схожих новостей: 9. Тональность: 0. Информативность: 9.18. Источник: www.scientific.net.