Adaptive Particle Swarm and Ant Colony Optimization Path Planning for Autonomous Robot Navigation
DOI:
https://doi.org/10.18196/jrc.v6i4.26853Keywords:
Path Planning, Hybrid Metaheuristics, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Adaptive Switching, Robot NavigationAbstract
Path planning in cluttered and uncertain environments remains a significant challenge in robotics, autonomous navigation, and logistics optimization. This paper proposes a novel Adaptive Hybrid PSO-ACO Planner, which synergistically combines Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to compute efficient paths in grid-based environments with static obstacles. Unlike traditional fixed-phase hybrids, our approach features a dynamic switching strategy between PSO and ACO based on real-time convergence behavior, allowing the algorithm to maintain progress and escape local minima. Additionally, adaptive parameter tuning is integrated to enhance the balance between global exploration and local exploitation throughout the search. The switching logic is governed by two criteria: a stagnation threshold that triggers phase transitions and a progress-dependent adaptation mechanism that adjusts search intensities over time. PSO dominates the early search phase, rapidly exploring the solution space, while ACO refines promising paths through pheromone-guided optimization in later stages. The proposed planner also includes a path reconstruction module to ensure solution completeness and robustness. Experimental evaluations on grid-based environments demonstrate that the proposed method consistently achieves higher path quality and faster convergence compared to standalone PSO and ACO approaches. Quantitative results demonstrate notable improvements in path efficiency and overall success rate across a range of obstacle densities. These advancements establish the Adaptive Hybrid PSOACO Planner as a robust and efficient tool for real-time and practical deployment in autonomous robot navigation systems.
References
M. Balza, M. A. Goldbarg, S. N. Silva, L. M. D. Silva and M. A. C. Fernandes, “A Real-Time Safe Navigation Proposal for Mobile Robots in Unknown Environments Using Meta-Heuristics,” in IEEE Access, vol. 13, pp. 23987-24013, 2025, doi: 10.1109/ACCESS.2025.3536081.
A. Moraga et al., “AI-Driven UAV and IoT Traffic Optimization: Large Language Models for Congestion and Emission Reduction in Smart Cities,” drones, vol. 9, no. 4, 2025, doi: 10.3390/drones9040248.
O. Hamed and M. Hamlich, “Navigation method for autonomous mobile robots based on ROS and multi-robot improved Q-learning,” Progress in Artificial Intelligence, 2024, doi: 10.1007/s13748-024-00320-5.
W. A. Hashim et al., “Optimizing Mobile Robot Path Planning with a Hybrid Crocodile Hunting and Falcon Optimization Algorithm,” Journal of Robotics and Control (JRC), vol. 6, no. 2, pp. 543–552, 2025, doi: 10.18196/jrc.v6i2.25586.
Y. Zhang, Y. Shen, Q. Wang, C. Song, N. Dai, and B. He, “A novel hybrid swarm intelligence algorithm for solving TSP and desired-path-based online obstacle avoidance strategy for AUV,” Robotics and Autonomous Systems, vol. 177, 2024, doi: 10.1016/j.robot.2024.104678.
P. Duan, Z. Yu, K. Gao, L. Meng, Y. Han, and F. Ye, “Solving the multiobjective path planning problem for mobile robot using an improved NSGA-II algorithm,” Swarm and Evolutionary Computation, vol. 87, 2024, doi: 10.1016/j.swevo.2024.101576.
N. Promkaew et al., “Development of metaheuristic algorithms for efficient path planning of autonomous mobile robots in indoor environments,” Results in Engineering, vol. 22, 2024, doi: 10.1016/j.rineng.2024.102280.
I. Saleh, N. Borhan, A. Yunus and W. Rahiman, “Comprehensive Technical Review of Recent Bio-Inspired Population-Based Optimization (BPO) Algorithms for Mobile Robot Path Planning,” in IEEE Access, vol. 12, pp. 20942-20961, 2024, doi: 10.1109/ACCESS.2024.3362638.
A. Khatib, O. Hamed, M. Hamlich, and A. Mouchtachi, “Enhancing Multi-Robot Systems Cooperation through Machine Learning-based Anomaly Detection in Target Pursuit,” Journal of Robotics and Control (JRC), vol. 5, no. 3, pp. 893–901, 2024, doi: 10.18196/jrc.v5i3.20333.
O. Hamed and M. Hamlich, “A novel approach for locating and hunting dynamic targets in unknown environments,” Progress in Artificial Intelligence, 2024, doi: 10.1007/s13748-024-00321-4.
S. H. Abood, H. M. H. Al-Khafaji, and M. M. H. Al-Khafaji, “Enhancing Collision Avoidance in Mobile Robots Using YOLOv5: A Lightweight Approach for Unstructured Environments,” Journal of Robotics and Control (JRC), vol. 6, no. 2, pp. 769–778, 2025, doi: 10.18196/jrc.v6i2.25856.
A. Senthilselvi, V. S. Varshini, E. Reena Sharan, T. Shruthi Shree, B. J. Chelliah and S. Senthil Pandi, “Load-balancing in Cloud Computing Environment using Hybrid Particle Swarm Optimization and Ant Colony Optimization Algorithm,” 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT), pp. 755-760, 2024, doi: 10.1109/ICAICCIT64383.2024.10912094.
Y. S. Alqudsi, R. A. A. Saleh, M. Makaraci, and H. M. Ertunc¸, “Enhancing aerial robots performance through robust hybrid control and metaheuristic optimization of controller parameters,” Neural Computing and Applications, vol. 36, pp. 413–424, 2024, doi: 10.1007/s00521-023-09014-w.
T. Jathunga and S. Rajapaksha, “Improved Path Planning for Multi-Robot Systems Using a Hybrid Probabilistic Roadmap and Genetic Algorithm Approach,” Journal of Robotics and Control (JRC), vol. 6, no. 2, pp. 715– 733, 2025, doi: 10.18196/jrc.v6i2.25572.
K. C. Ugwoke, N. A. Nnanna, and S. E.-Y. Abdullahi, “Simulation-based review of classical, heuristic, and metaheuristic path planning algorithms,” Scientific Reports, vol. 15, 2025, doi: 10.1038/s41598-025-96614-2.
O. Hamed and M. Hamlich, “Improvised multi-robot cooperation strategy for hunting a dynamic target,” 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), pp. 1-4, 2020, doi: 10.1109/ISAECT50560.2020.9523684.
M. T. Hameed, F. A. Raheem, and A. R. Nasser, “Enhanced RRT ∗ with APF and Halton Sequence for Robot Path Planning,” Journal of Robotics and Control (JRC), vol. 6, no. 2, pp. 493–513, 2025, doi: 10.18196/jrc.v6i2.24921.
M. M. Quamar and S. ElFerik, “Control and Coordination for Swarm of UAVs Under Multi-Predator Attack,” 2023 Systems and Information Engineering Design Symposium (SIEDS), pp. 96-101, 2023, doi: 10.1109/SIEDS58326.2023.10137788.
A. Keymasi-Khalaji, P. Mokhtari, and F. Bathaei, “Predictive control for the navigation of spherical robots in obstacle-rich environments,” Scientific Reports, vol. 15, 2025, doi: 10.1038/s41598-025-96521-6.
T. Zhou and W. Wei, “Mobile robot path planning based on an improved ACO algorithm and path optimization,” Multimedia Tools and Applications, vol. 84, pp. 10899–10922, 2025, doi: 10.1007/s11042-024-19370-x.
Y. Msala, O. Hamed, M. Talea, and M. Aboulfatah, “A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks,” Journal of Robotics and Control (JRC), vol. 4, no. 6, pp. 743–753, 2023, doi: 10.18196/jrc.v4i6.18650.
F. F. Rad, P. Oghazi, ˙I. Onur, and A. Kordestani, “Adoption of AI-based order picking in warehouse: benefits, challenges, and critical success factors,” Review of Managerial Science, 2025, doi: 10.1007/s11846-025-00858-1.
M. Chaikovskaia, J.-P. Gayon, and A. Quilliot, “Optimization of a fleet of reconfigurable robots,” Flexible Services and Manufacturing Journal, 2025, doi: 10.1007/s10696-025-09596-8.
A. Singh, V. Kalaichelvi, and R. Karthikeyan, “Machine learning-based multi-sensor fusion for warehouse robot in GPS-denied environment,” Multimedia Tools and Applications, vol. 83, pp. 56229–56246, 2024, doi: 10.1007/s11042-023-17753-0.
M. S. M. Moreira, D. K. D. Villa, and M. Sarcinelli-Filho, “Controlling a Virtual Structure Involving a UAV and a UGV for Warehouse Inventory,” Journal of Intelligent & Robotic Systems, vol. 110, no. 21, 2024, doi: 10.1007/s10846-024-02134-y.
J. F. S and S. R, “Self-adaptive learning particle swarm optimization-based path planning of mobile robot using 2D Lidar environment,” Robotica, vol. 42, no. 4, pp. 977–1000, 2024, doi: 10.1017/S0263574723001819.
J. A. Abdulsaheb and D. J. Kadhim, “Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey,” Robotics, vol. 12, no. 4, 2023, doi: 10.3390/robotics12040093.
P. Soustek, R. Matousek, J. Dvorak, and L. Manakova, “Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid,” MENDEL, vol. 28, no. 2, pp. 97–107, 2022, doi: 10.13164/mendel.2022.2.097.
J. Zhaozhen, W. Wenlong, S. Xuehai, L. Qiang, and L. Ming, “Path Planning Method for Marine Dynamic Target Coverage Search,” in Advances in Guidance, Navigation and Control, vol. 1349, pp. 57–66, 2025, doi: 10.1007/978-981-96-2248-1 6.
H. Heng and W. Rahiman, “ACO-GA-Based Optimization to Enhance Global Path Planning for Autonomous Navigation in Grid Environments,” in IEEE Transactions on Evolutionary Computation, 2025, doi: 10.1109/TEVC.2025.3543401.
A. Carbognin, L. L. Custode, and G. Iacca, “Genetic Improvement of TCP Congestion Avoidance,” in Bioinspired Optimization Methods and Their Applications, vol. 13627, pp. 114–126, 2022, doi: 10.1007/978-3- 031-21094-5 9.
Z. Cai, J. Liu, L. Xu, and J. Wang, “Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm,” Robotics and Autonomous Systems, vol. 179, 2024, doi: 10.1016/j.robot.2024.104748.
S. Hachani and E. Nechadi, “Type-2 Fuzzy Logic-Based Robot Navigation in Uncertain Environments: Simulation and Real-World Implementation,” Journal of Robotics and Control (JRC), vol. 6, no. 1, pp. 437–445, 2024, doi: 10.18196/jrc.v6i1.25553.
M. S. Saleh, Y. I. A. Mashhadany, M. Alshaibi, F. M. Ameen, and S. Algburi, “Optimal Mobile Robot Navigation for Obstacle Avoidance Based on ANFIS Controller,” Journal of Robotics and Control (JRC), vol. 6, no. 1, pp. 484–492, 2024, doi: 10.18196/jrc.v6i1.24882.
M. Haris and H. Nam, “Path Planning Optimization of Smart Vehicle With Fast Converging Distance-Dependent PSO Algorithm,” in IEEE Open Journal of Intelligent Transportation Systems, vol. 5, pp. 726-739, 2024, doi: 10.1109/OJITS.2024.3486155.
B. Guo, Y. Sun, and Y. Chen, “Safe path planning of mobile robot based on improved particle swarm optimization,” Transactions of the Institute of Measurement and Control, vol. 47, no. 9, 2024, doi: 10.1177/01423312241264860.
H. T. Najm, A. , Nur Syazreen, and A. S. and Al-Araji, “Enhanced path planning algorithm via hybrid WOA-PSO for differential wheeled mobile robots,” Systems Science & Control Engineering, vol. 12, no. 1, 2024, doi: 10.1080/21642583.2024.2334301.
Y. Yang et al., “UAV Formation Trajectory Planning Algorithms: A Review,” drones, vol. 7, no. 1, 2023, doi: 10.3390/drones7010062.
T. Sutikno, “The future of artificial intelligence-driven robotics: Applications and implications,” IAES International Journal of Robotics and Automation (IJRA), vol. 13, no. 4, pp. 361–372, 2024, doi: 10.11591/ijra.v13i4.pp 361-372.
K. Mohammed, A. Aliedani, and A. Al-Ibadi, “Adaptive Vector Field Histogram Plus (VFH+) Algorithm using Fuzzy Logic in Motion Planning for Quadcopter,” Journal of Robotics and Control (JRC), vol. 5, no. 2, pp. 582–596, 2024, doi: 10.18196/jrc.v5i2.21540.
J. Akshya et al., “Metaheuristic Optimization for Path Planning in UAV Networks for Long-Distance Inspection Tasks,” 2024 13th International Conference on System Modeling & Advancement in Research Trends (SMART), pp. 680-687, 2024, doi: 10.1109/SMART63812.2024.10882533.
H. Lei, Y. Yan, J. Liu, Q. Han and Z. Li, “Hierarchical Multi-UAV Path Planning for Urban Low Altitude Environments,” in IEEE Access, vol. 12, pp. 162109-162121, 2024, doi: 10.1109/ACCESS.2024.3483943.
W. A. H. Sandanika, S. H. Wishvajith, S. Randika, D. A. Thennakoon, S. K. Rajapaksha, and V. Jayasinghearachchi, “ROS-based Multi-Robot System for Efficient Indoor Exploration Using a Combined Path Planning Technique,” Journal of Robotics and Control (JRC), vol. 5, no. 5, pp. 1241–1260, 2024, doi: 10.18196/jrc.v5i5.22494.
Z. Wang, H. Yan, Y. Wang, Z. Xu, Z. Wang and Z. Wu, “Research on Autonomous Robots Navigation based on Reinforcement Learning,” 2024 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), pp. 78-81, 2024, doi: 10.1109/RAIIC61787.2024.10671357.
S. Prakash, A. P. Sami, and B. Sharma, “Enhancing Path Planning with Obstacles Via a Combined Dijkstra-LbCS Methodology,” in Computing and Machine Learning, vol. 1108, pp. 47–59, 2024, doi: 10.1007/978- 981-97-6588-1 4.
L. Xie and X. Zhang, “Obstacle Avoidance for UAV Swarm Based on Pigeon Flock Dynamic Interaction Mechanism,” 2024 China Automation Congress (CAC), pp. 3429-3434, 2024, doi: 10.1109/CAC63892.2024.10864730.
M. Baziyad, N. AbuJabal, R. Fareh, T. Rabie, I. Kamel and M. Bettayeb, “A Direction for Swarm Robotic Path Planning Technique Using Potential Field Concepts and Particle Swarm Optimization,” 2023 15th International Conference on Innovations in Information Technology (IIT), pp. 7-12, 2023, doi: 10.1109/IIT59782.2023.10366467.
M. M. Quamar and S. El Ferik, “Cooperative Prey Hunting for Multi Agent System Designed using Bio-Inspired adaptation Technique,” 2023 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 1-6, 2023, doi: 10.1109/ICCAD57653.2023.10152302.
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, 2023, doi: 10.1016/j.jocs.2022.101938.
V. N. Siron Santhiya and J. Simon, “A Novel Optimal Path Planning Procedure for Autonomous Underwater Vehicles,” 2024 Second International Conference on Advances in Information Technology (ICAIT), pp. 1-6, 2024, doi: 10.1109/ICAIT61638.2024.10690813.
I. Dagal, B. Akın, and E. Akboy, “MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization algorithm for battery charging through simulink,” Scientific Reports, vol. 12, 2022, doi: 10.1038/s41598-022-06609-6.
M. Lazreg and N. Benamrane, “Hybrid system for optimizing the robot mobile navigation using ANFIS and PSO,” Robotics and Autonomous Systems, vol. 153, 2022, doi: 10.1016/j.robot.2022.104114.
J. Zhou, Z. Zhang, Q. Zhong and J. Li, “A Semifixed Clustering Routing Protocol Based on Improved ACO Algorithm for WSNs,” in IEEE Sensors Journal, vol. 24, no. 21, pp. 34664-34675, 2024, doi: 10.1109/JSEN.2024.3416961.
M. Garouani, A. Ahmad, M. Bouneffa, and M. Hamlich, “AutoencoderkNN meta-model based data characterization approach for an automated selection of AI algorithms,” Journal of Big Data, vol. 10, no. 14, 2023, doi: 10.1186/s40537-023-00687-7.
M. Chaabi, M. Hamlich, and M. Garouani, “Product defect detection based on convolutional autoencoder and one-class classification,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 12, no. 2, pp. 912–920, 2023, doi: 10.11591/ijai.v12.i2.pp912-920.
A. S. B. Shahadat, M. a. H. Akhand, and M. A. S. Kamal, “Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem,” Mathematics, vol. 10, no. 4, 2022, doi: 10.3390/math10142448.
H. Ma, D. Wang, J. Ren, and J. Qiao, “Self-organizing neural intelligent control for nonlinear discrete-time systems with particle swarm optimization,” Nonlinear Dynamics, vol. 113, pp. 583–595, 2025, doi: 10.1007/s11071-024-10173-1.
N. K. Ayoob et al., “Articulated Robot Path Planning Based on Hybridization of Adaptive Dynamic Environments,” Karbala International Journal of Modern Science, vol. 11, no. 2, 2025, doi: 10.33640/2405-609X.3398.
S. M. J. Alzubairi, A. Petunin, and A. J. Humaidi, “Multi-robot task allocation based on an automatic clustering strategy employing an enhanced dynamic distributed PSO,” International Review of Applied Sciences and Engineering, 2025, doi: 10.1556/1848.2025.00935.
B. A. Muthu and C. Cherubini, “Underwater Digital Twin Sensor Network-Based Maritime Communication and Monitoring Using Exponential Hyperbolic Crisp Adaptive Network-Based Fuzzy Inference System,” Water, vol. 17, no. 9, 2025, doi: 10.3390/w17091324.
M. Ragab, E. B. Ashary, W. H. Aljedaibi, I. R. Alzahrani, A. Kumar, D. Gupta, and R. F. Mansour, “A novel metaheuristics with adaptive neuro-fuzzy inference system for decision making on autonomous unmanned aerial vehicle systems,” ISA Transactions, vol. 132, pp. 16–23, 2023, doi: 10.1016/j.isatra.2022.04.006.
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.
R. Priyadarshi and R. R. Kumar, “Evolution of Swarm Intelligence: A Systematic Review of Particle Swarm and Ant Colony Optimization Approaches in Modern Research,” Archives of Computational Methods in Engineering, 2025, doi: 10.1007/s11831-025-10247-2.
T. Ren, T. Luo, B. Jia, B. Yang, L. Wang, and L. Xing, “Improved ant colony optimization for the vehicle routing problem with split pickup and split delivery,” Swarm and Evolutionary Computation, vol. 77, 2023, doi: 10.1016/j.swevo.2023.101228.
S. Lin, A. Liu, J. Wang, and X. Kong, “A Review of Path-Planning Approaches for Multiple Mobile Robots,” Machines, vol. 10, no. 9, 2022, doi: 10.3390/machines10090773.
Y. Chen, G. Bai, Y. Zhan, X. Hu and J. Liu, “Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning,” in IEEE Access, vol. 9, pp. 40728-40742, 2021, doi: 10.1109/ACCESS.2021.3062375.
A. Kumar, S. Tiwari, and A. Majumder, “A*-VG algorithm: A hybrid algorithm for the path planning of inspection robots,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 45, no. 386, 2023, doi: 10.1007/s40430-023-04249-z.
T. Xiong, H. Li, K. Ding, H. Liu, and Q. Li, “A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning,” Drones, vol. 7, no. 10, 2023, doi: 10.3390/drones7100633.
X. Zhao, Y. Sun, Y. Li, N. Jia, and J. Xu, “Applications of machine learning in real-time control systems: A review,” Measurement Science and Technology, vol. 36, 2024, doi: 10.1088/1361-6501/ad8947.
H. Suwoyo, A. Adriansyah, J. Andika, A. U. Shamsudin, and Y. Tian, “An Effective Way for Repositioning the Beacon Nodes of Fast RRT Results Utilizing Grey Wolf Optimization,” Journal of Robotics and Control (JRC), vol. 6, no. 1, pp. 272–284, 2024, doi: 10.18196/jrc.v6i1.22062.
N. Gupta and A. Mathur, “Performance Comparison of PSO, ACO and Hybrid ACO-PSO for Multi-Target Search Using Autonomous Swarm Drones in Obstacle-Rich Environments,” 2025 8th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), pp. 1-6, 2025, doi: 10.1109/IEMENTech65115.2025.10959663.
J. Chung, J. Fayyad, Y. A. Younes, and H. Najjaran, “Learning teambased navigation: A review of deep reinforcement learning techniques for multi-agent pathfinding,” Artificial Intelligence Review, vol. 57, no. 41, 2024, doi: 10.1007/s10462-023-10670-6.
M. Reda, A. Onsy, A. Y. Haikal, and A. Ghanbari, “Path planning algorithms in the autonomous driving system: A comprehensive review,” Robotics and Autonomous Systems, vol. 174, 2024, doi: 10.1016/j.robot.2024.104630.
L. Wang, L. Liu and X. Lu, “Robot Path Planning Based on Generative Learning Particle Swarm Optimization,” in IEEE Access, vol. 12, pp. 130063-130072, 2024, doi: 10.1109/ACCESS.2024.3457957.
X. Yu, B. Su, Z. Wang, J. Zhang and C. Zhang, “Cognitive Robotics: Enhancing Multirobot Target Search in Unknown Environments Through Adaptive Communication Strategies,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 55, no. 5, pp. 3449-3463, 2025, doi: 10.1109/TSMC.2025.3540059.
O. Hamed and M. Hamlich, “Hybrid Formation Control for MultiRobot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient,” MENDEL, vol. 27, no. 2, pp. 23–29, 2021, doi: 10.13164/mendel.2021.2.023.
X. Zhou, H. Ma, J. Gu, H. Chen, and W. Deng, “Parameter adaptationbased ant colony optimization with dynamic hybrid mechanism,” Engineering Applications of Artificial Intelligence, vol. 114, 2022, doi: 10.1016/j.engappai.2022.105139.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942-1948, 1995, doi: 10.1109/ICNN.1995.488968.
M. Dorigo and L. M. Gambardella, “Ant colonies for the travelling salesman problem,” Biosystems, vol. 43, no. 2, pp. 73–81, 1997, doi: 10.1016/S0303-2647(97)01708-5.
H. Lu et al., “Two-layer path planning framework for WMRs in dynamic environments: Optimized ant colony algorithm and dynamic window approach,” Transactions of the Institute of Measurement and Control, 2025, doi: 10.1177/01423312241296969.
J. Li, L. Wan, Z. Huang, Y. Chen, and H. Tang, “Hybrid Path Planning Strategy Based on Improved Particle Swarm Optimisation Algorithm Combined with DWA for Unmanned Surface Vehicles,” Journal of Marine Science and Engineering, vol. 12, no. 8, pp. 1–16, 2024, doi: 10.3390/jmse12081268.
M. Wu, G. Li, J. Liao, H. Wang, W. Liu, X. Yan, M. Yang, and S. Li, “Multi-strategy hybrid adaptive dung beetle optimization for UAV photogrammetric 3D path planning under complex constraints,” Scientific Reports, vol. 15, 2025, doi: 10.1038/s41598-025-98563-2.
J. Xin, J. Kim, S. Chu, and N. Li, “OkayPlan: Obstacle Kinematics Augmented Dynamic real-time path Planning via particle swarm optimization,” Ocean Engineering, vol. 303, 2024, doi: 10.1016/j.oceaneng.2024.117841.
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