Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm
DOI:
https://doi.org/10.18196/jrc.v6i2.25586Keywords:
Path Planning, Hybrid Algorithm, Crocodile Hunting Search (CHS), Falcon Optimization (FO), Obstacle AvoidanceAbstract
Thorough path planning is critical in unmanned ground vehicle control to reduce path length, computational time, and the number of collisions. This paper aims to introduce a new metaheuristic method called the Hybrid Crocodile Hunting-SearcH and Falcon Optimization (CHS-FO) algorithm. This method combines CHS's exploration and exploitation abilities with FO's rapid convergence rate. In this way, the use of both metaheuristic techniques limits the disadvantage of the individual approach, guaranteeing a high level of both global and local search. We conduct several simulations to compare the performance of the CHS-FO algorithm with conventional algorithms such as A* and Genetic Algorithms (GA). It is found The results show that the CHS-FO algorithm performs 30–50% better in terms of computation time, involves shorter path planning, and improves obstacle avoidance. Eristic also suggests that the path generation algorithm can adapt to environmental constraints and be used in real-world scenarios, such as automating product movement in a warehouse or conducting search and rescue operations for lost vehicles. The primary The proposed CHS-FO architecture makes the robot more independent and better at making choices, which makes it a good choice for developing the next generation of mobile robotic platforms. Goals will encompass the improvement of the algorithm's scalability for use in multiple robots, as well as the integration of the algorithm in a real environment in real time.
References
A. K. Guruji, H. Agarwal, and D. K. Parsediya, “Time-efficient A* algorithm robot path planning,” Procedia Technology, vol. 23, pp. 144-149, 2016.
C. Han and B. Li, “Mobile robot path planning based on improved A* algorithm,” in 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 11, pp. 672-676, 2023.
S. Lin, A. Liu, J. Wang, and X. Kong, “An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse,” Journal of Computational Science, vol. 67, p. 101938, 2023.
H. Liu, Y. Shen, S. Yu, Z. Gao, and T. Wu, “Deep reinforcement learning for mobile robot path planning,” arXiv preprint arXiv:2404.06974, 2024.
L. Chen et al., "Transformer-Based Imitative Reinforcement Learning for Multirobot Path Planning," in IEEE Transactions on Industrial Informatics, vol. 19, no. 10, pp. 10233-10243, Oct. 2023, doi: 10.1109/TII.2023.3240585.
Z. Wu, J. Dai, B. Jiang, and H. R. Karimi, “Robot path planning based on artificial potential field with deterministic annealing,” ISA transactions, vol. 138, pp. 74-87, 2023.
S. W. Kareem, “A nature-inspired metaheuristic optimization algorithm based on crocodiles hunting search (CHS),” International Journal of Swarm Intelligence Research (IJSIR), vol. 13, no. 1, pp. 1-23, 2022.
H. Q. Awla, S. W. Kareem, and A. S. Mohammed, “A comparative evaluation of bayesian networks structure learning using falcon optimization algorithm,” IJIMAI, vol. 8, no. 2, pp. 81-87, 2023.
Y. Li, J. Zhao, Z. Chen, G. Xiong, and S. Liu, “A robot path planning method based on improved genetic algorithm and improved dynamic window approach,” Sustainability, vol. 15, no. 5, p. 4656, 2023.
L. Wu, X. Huang, J. Cui, C. Liu, and W. Xiao, “Modified adaptive ant colony optimization algorithm and its application for solving path planning of mobile robot,” Expert Systems with Applications, vol. 215, p. 119410, 2023.
L. Zhang, X. Shi, Y. Yi, L. Tang, J. Peng, and J. Zou, “Mobile robot path planning algorithm based on RRT_connect,” Electronics, vol. 12, no. 11, p. 2456, 2023.
C. Huang, Y. Zhao, M. Zhang, and H. Yang, "APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning," in IEEE Access, vol. 11, pp. 43238-43256, 2023, doi: 10.1109/ACCESS.2023.3272223.
S. W. Kareem, A. S. Mohammed, and F. S. Khoshabaa, “Novel nature-inspired meta-heuristic optimization algorithm based on hybrid dolphin and sparrow optimization,” International Journal of Nonlinear Analysis and Applications, vol. 14, no. 1, pp. 355-373, 2023.
K. Shi, Z. Wu, B. Jiang, and H. R. Karimi, “Dynamic path planning of mobile robot based on improved simulated annealing algorithm,” Journal of the Franklin Institute, vol. 360, no. 6, pp. 4378-4398, 2023.
R. Akay and M. Y. Yildirim, “Multi-strategy and self-adaptive differential sine–cosine algorithm for multi-robot path planning,” Expert Systems with Applications, vol. 232, p. 120849, 2023.
J. Jin, Y. Zhang, Z. Zhou, M. Jin, X. Yang, and F. Hu, “Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments,” Computers and Electrical Engineering, vol. 105, p. 108473, 2023.
E. García, J. R. Villar, Q. Tan, J. Sedano, and C. Chira, “An efficient multi-robot path planning solution using A* and coevolutionary algorithms,” Integrated Computer-Aided Engineering, vol. 30, no. 1, pp. 41-52, 2023.
X. Wang, Z. Liu, and J. Liu, “Mobile robot path planning based on an improved A* algorithm,” in International conference on computer graphics, artificial intelligence, and data processing (ICCAID 2022), vol. 12604, pp. 1093-1098, 2023.
N. S. Abu, W. M. Bukhari, M. H. Adli, and A. Ma’arif, “Optimization of an Autonomous Mobile Robot Path Planning Based on Improved Genetic Algorithms,” Journal of Robotics and Control (JRC), vol. 4, no. 4, pp. 557-571, 2023.
S. S. Qasim and S. M. NSAIF, “Advancements in Time Series-Based Detection Systems for Distributed Denial-of-Service (DDoS) Attacks: A Comprehensive Review,” Babylonian Journal of Networking, vol. 2024, pp. 9–17, Jan. 2024, doi: 10.58496/bjn/2024/002.
R. Maity, R. Mishra, and P. K. Pattnaik, “Flying robot path planning techniques and its trends,” Materials Today: Proceedings, vol. 80, pp. 2187-2192, 2023.
S. R. Ahmed et al., “Optimal Elevators Control for Buildings Based on Associative Memory,” in 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-8, 2024.
Z. Yu, J. Yuan, Y. Li, C. Yuan, and S. Deng, “A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm,” Computers and Electrical Engineering, vol. 109, p. 108730, 2023.
Y. Ou, P. Yin, and L. Mo, “An improved grey wolf optimizer and its application in robot path planning,” Biomimetics, vol. 8, no. 1, p. 84, 2023.
Y. Gu, Z. Zhu, J. Lv, L. Shi, Z. Hou, and S. Xu, “DM-DQN: Dueling Munchausen deep Q network for robot path planning,” Complex & Intelligent Systems, vol. 9, no. 4, pp. 4287-4300, 2023.
J. Cai, A. Du, X. Liang, and S. Li, “Prediction-based path planning for safe and efficient human–robot collaboration in construction via deep reinforcement learning,” Journal of Computing in Civil Engineering, vol. 37, no. 1, p. 04022046, 2023.
G. Ma, Y. Duan, M. Li, Z. Xie, and J. Zhu, “A probability smoothing Bi-RRT path planning algorithm for indoor robot,” Future Generation Computer Systems, vol. 143, pp. 349-360, 2023.
Y. Chen, J. Wu, C. He, and S. Zhang, “Intelligent warehouse robot path planning based on improved ant colony algorithm,” IEEE Access, vol. 11, pp. 12360-12367, 2023.
R. I. Kadhim et al., “AI-enhanced Cybersecurity in Fluid Dynamics Simulation Software,” in 2024 8th International Symposium on Innovative Approaches in Smart Technologies (ISAS), pp. 1-8, 2024.
G. Li, C. Liu, L. Wu, and W. Xiao, “A mixing algorithm of ACO and ABC for solving path planning of mobile robot,” Applied Soft Computing, vol. 148, p. 110868, 2023.
Y. Huang, H. Wang, L. Han, and Y. Xu, “Robot path planning in narrow passages based on improved PRM method,” Intelligent Service Robotics, pp. 1-12, 2024.
S. Lin, A. Liu, J. Wang, and X. Kong, “An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning,” Expert Systems with Applications, vol. 237, p. 121510, 2024.
B. Fu, L. Chen, Y. Zhou, D. Zheng, Z. Wei, J. Dai, and H. Pan, “An improved A* algorithm for the industrial robot path planning with high success rate and short length,” Robotics and Autonomous Systems, vol. 106, pp. 26-37, 2018.
X. Xu, J. Zeng, Y. Zhao, and X. Lü, “Research on global path planning algorithm for mobile robots based on improved A,” Expert Systems with Applications, vol. 243, p. 122922, 2024.
C. Li, X. Huang, J. Ding, K. Song, and S. Lu, “Global path planning based on a bidirectional alternating search A* algorithm for mobile robots,” Computers & Industrial Engineering, vol. 168, p. 108123, 2022.
J. Cui, L. Wu, X. Huang, D. Xu, C. Liu, and W. Xiao, “Multi-strategy adaptable ant colony optimization algorithm and its application in robot path planning,” Knowledge-Based Systems, vol. 288, p. 111459, 2024.
J. Gao, W. Ye, J. Guo, and Z. Li, “Deep reinforcement learning for indoor mobile robot path planning,” Sensors, vol. 20, no. 19, p. 5493, 2020.
J. G. Kang, D. W. Lim, Y. S. Choi, W. J. Jang, and J. W. Jung, “Improved RRT-connect algorithm based on triangular inequality for robot path planning,” Sensors, vol. 21, no. 2, p. 333, 2021.
T. Xu, H. Zhou, S. Tan, Z. Li, X. Ju, and Y. Peng, “Mechanical arm obstacle avoidance path planning based on improved artificial potential field method,” Industrial Robot: the international journal of robotics research and application, vol. 49, no. 2, pp. 271-279, 2022.
L. Abualigah, M. Abd Elaziz, P. Sumari, Z. W. Geem, and A. H. Gandomi, “Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer,” Expert Systems with Applications, vol. 191, p. 116158, 2022.
J. Hou, W. Jiang, Z. Luo, L. Yang, X. Hu, and B. Guo, “Dynamic path planning for mobile robots by integrating improved sparrow search algorithm and dynamic window approach,” Actuators, vol. 13, no. 1, p. 24, 2024.
R. Akay and M. Y. Yildirim, “Multi-strategy and self-adaptive differential sine–cosine algorithm for multi-robot path planning,” Expert Systems with Applications, vol. 232, p. 120849, 2023.
F. H. Ajeil, I. K. Ibraheem, M. A. Sahib, and A. J. Humaidi, “Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm,” Applied Soft Computing, vol. 89, p. 106076, 2020.
S. J. Al-Kamil and R. Szabolcsi, “Optimizing path planning in mobile robot systems using motion capture technology,” Results in Engineering, vol. 22, p. 102043, 2024.
C. Liu, L. Wu, W. Xiao, G. Li, D. Xu, J. Guo, and W. Li, “An improved heuristic mechanism ant colony optimization algorithm for solving path planning,” Knowledge-based systems, vol. 271, p. 110540, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Wassan Adnan Hashim, Saadaldeen Rashid Ahmed, Mohammed Thakir Mahmood, Mohammed Amin Almaiah, Rami Shehab, Rommel AlAli
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This journal is based on the work at https://journal.umy.ac.id/index.php/jrc under license from Creative Commons Attribution-ShareAlike 4.0 International License. You are free to:
- Share – copy and redistribute the material in any medium or format.
- Adapt – remix, transform, and build upon the material for any purpose, even comercially.
The licensor cannot revoke these freedoms as long as you follow the license terms, which include the following:
- Attribution. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
• Creative Commons Attribution-ShareAlike (CC BY-SA)
JRC is licensed under an International License
