A Review of Path Planning Algorithms for Mobile Robots
Path planning for mobile robots is a key technology for achieving autonomous navigation. It aims to search for a collision-free optimal path from the starting point to the target point in complex environments. This paper provides a systematic review of existing path planning algorithms. First, according to the principles of path planning algorithms, the existing methods are categorized into classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Second, the basic principles, advantages, disadvantages, and improvement strategies proposed by other scholars to address the limitations of each type of algorithm are elaborated in detail. Finally, based on the trend of algorithm fusion, the development potential of algorithm fusion mechanisms is prospected. The review concludes that a single algorithm struggles to adapt to complex and dynamic environments, and the integration of the advantages of multiple algorithms has become the current research trend. In the future, improving the real-time performance and robustness of algorithms in dynamic and unstructured environments remains a major challenge.
[1] Wu J, Yan J, Zhang F, et al., 2026, A Review of Research on Path Planning Algorithms for Mobile Robots. Application Research of Computers, 1–13.
[2] Dias P, Carvalho de Souza J, Solteiro P, et al., 2025, Robot Path Planning: From Analytical to Computer Intelligence Approaches. Journal of Intelligent & Robotic Systems, 111(4): 112.
[3] Niloy M, Shama A, Chakrabortty R, et al., 2021, Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review. IEEE Access, 9: 35338–35370.
[4] Liu L, Wang X, Yang X, et al., 2023, Path Planning Techniques for Mobile Robots: Review and Prospect. Expert Systems with Applications, 227: 120254.
[5] Zhang L, Li Y, 2021, Mobile Robot Path Planning Algorithm Based on Improved A*. Journal of Physics: Conference Series, 1848(1): 012013.
[6] Luo W, Zhang C, Zheng W, et al., 2025, Review and Outlook of Path Planning Algorithm System for Mobile Robots. Computer Engineering and Applications, 1–18.
[7] Luo M, Hou X, Yang J, 2020, Surface Optimal Path Planning Using an Extended Dijkstra Algorithm. IEEE Access, 8: 147827–147838.
[8] Li X, 2021, Path Planning of Intelligent Mobile Robot Based on Dijkstra Algorithm. Journal of Physics: Conference Series, 2083(4): 042034.
[9] Gong H, Ni C, Wang P, et al., 2024, Smooth Path Planning Method Based on the Dijkstra Algorithm. Journal of Beijing University of Aeronautics and Astronautics, 50(2): 535–541.
[10] Ahmad J, Nadhir Ab Wahab M, 2025, Enhancing the Safety and Smoothness of Path Planning Through an Integration of Dijkstra’s Algorithm and Piecewise Cubic Bézier Optimization. Expert Systems with Applications, 289: 128315.
[11] Zhao J, Zhang Y, Ma Z, et al., 2018, Improvement and Verification of A* Algorithm for AGV Path Planning. Computer Engineering and Applications, 54(21): 217–223.
[12] Ou Y, Fan Y, Zhang X, et al., 2022, Improved A* Path Planning Method Based on the Grid Map. Sensors, 22(16): 6198.
[13] Yin C, Tan C, Wang C, et al., 2024, An Improved A-Star Path Planning Algorithm Based on Mobile Robots in Medical Testing Laboratories. Sensors, 24(6): 1784.
[14] Chen S, Yang G, Cui G, et al., 2025, Improved Path Planning and Controller Design Based on PRM. IEEE Access, 13: 44156–44168.
[15] Liu C, Xie S, Sui X, et al., 2023, PRM-D* Method for Mobile Robot Path Planning. Sensors, 23(7): 3512.
[16] Francis A, Faust A, Chiang H, et al., 2020, Long-Range Indoor Navigation With PRM-RL. IEEE Transactions on Robotics, 36(4): 1115–1134.
[17] Ye L, Chen J, Zhou Y, 2022, Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment. Frontiers in Neurorobotics, 16: 910859.
[18] Wang K, Zeng G, Lu D, et al., 2019, Mobile Robot Path Planning Algorithm Based on Improved Asymptotically Optimal Bidirectional Rapidly-Exploring Random Tree. Journal of Computer Applications, 39(5): 1312–1317.
[19] Khuat T, Bui D, Nguyen H, et al., 2025, Multi-Goal Rapidly Exploring Random Tree With Safety and Dynamic Constraints for UAV Cooperative Path Planning. IEEE Transactions on Vehicular Technology, 74(9): 13446–13457.
[20] Yu Z, Xiang L, 2021, NPQ-RRT*: An Improved RRT* Approach to Hybrid Path Planning. Complexity, 2021(1): 6633878.
[21] Bian P, Fan J, Liu Y, et al., 2025, Quad Rapidly-Exploring Random Tree Star Algorithm with Improved Potential Force for Unmanned Aerial Vehicle Path Planning. IEEE Access, 13: 111187–111198.
[22] Bi Y, Fang X, 2025, A Hybrid Path Planning Framework Integrating Deep Reinforcement Learning and Variable-Direction Potential Fields. Mathematics, 13(14): 2312.
[23] Ma B, Ji Y, Fang L, 2025, A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field. Drones, 9(6): 391–421.
[24] Yang J, Zhang H, Ning P, 2023, Path Planning and Trajectory Optimization Based on Improved APF and Multi-Target. IEEE Access, 11: 139121–139132.
[25] Zhai L, Liu C, Zhang X, et al., 2024, Local Trajectory Planning for Obstacle Avoidance of Unmanned Tracked Vehicles Based on Artificial Potential Field Method. IEEE Access, 12: 19665–19681.
[26] Jin F, Ye Z, Li M, et al., 2025, A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search. Sensors, 25(9): 2796.
[27] Kobayashi M, Zushi H, Nakamura T, et al., 2023, Local Path Planning: Dynamic Window Approach With Q-Learning Considering Congestion Environments for Mobile Robot. IEEE Access, 11: 96733–96742.
[28] Chang X, Wang J, Li K, et al., 2025, Research on Multi-UAV Autonomous Obstacle Avoidance Algorithm Integrating Improved Dynamic Window Approach and ORCA. Scientific Reports, 15(1): 14646.
[29] Gong X, Gao Y, Wang F, et al., 2024, A Local Path Planning Algorithm for Robots Based on Improved DWA. Electronics, 13(15): 2965.
[30] Duan Q, 2026, Path Planning for Unmanned Surface Vehicle Based on D and DWA. Control Engineering of China, 33(1): 129–134.
[31] Tao Y, Wen Y, Gao H, et al., 2022, A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm. Electronics, 11(8): 1192.
[32] Li J, Hu Y, Yang S, 2025, A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments. IEEE Transactions on Evolutionary Computation, 29(2): 375–389.
[33] Li K, Hu Q, Liu J, 2021, Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm. Wireless Communications and Mobile Computing, 2021: 8836615.
[34] Ab Wahab M, Nazir A, Khalil A, et al., 2024, Improved Genetic Algorithm for Mobile Robot Path Planning in Static Environments. Expert Systems with Applications, 249: 123762.
[35] Feng T, Li J, Jiang H, et al., 2024, The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm in Different Tasks and Complex Road Environments. IEEE Access, 12: 18400–18415.
[36] Yang L, Fu L, Li P, et al., 2022, LF-ACO: An Effective Formation Path Planning for Multi-Mobile Robot. Mathematical Biosciences and Engineering, 19(1): 225–252.
[37] Zhang C, Ma J, Wang X, et al., 2025, NMS-EACO: A Novel Multi-Strategy ACO for Mobile Robot Path Planning. Electronics, 14(17): 3440.
[38] Zhao L, Li F, Sun D, et al., 2024, An Improved Ant Colony Algorithm Based on Q-Learning for Route Planning of Autonomous Vehicle. International Journal of Computers Communications & Control, 19(3): 5382.
[39] Li P, Wei L, Wu D, 2025, An Intelligently Enhanced Ant Colony Optimization Algorithm for Global Path Planning of Mobile Robots in Engineering Applications. Sensors, 25(5): 1326.
[40] Shi Y, Zhang H, Li Z, et al., 2023, Path Planning for Mobile Robots in Complex Environments Based on Improved Ant Colony Algorithm. Mathematical Biosciences and Engineering, 20(9): 15568–15602.
[41] Huang C, Zhao Y, Zhang M, et al., 2023, APSO: An A*-PSO Hybrid Algorithm for Mobile Robot Path Planning. IEEE Access, 11: 43238–43256.
[42] Tao B, Kim J, 2024, Mobile Robot Path Planning Based on Bi-Population Particle Swarm Optimization with Random Perturbation Strategy. Journal of King Saud University – Computer and Information Sciences, 36(2): 101974.
[43] Lian J, Yu W, Xiao K, et al., 2020, Cubic Spline Interpolation-Based Robot Path Planning Using a Chaotic Adaptive Particle Swarm Optimization Algorithm. Mathematical Problems in Engineering, 2020: 1–20.
[44] Lu C, Yang J, Leira B, et al., 2023, Three-Dimensional Path Planning of Deep-Sea Mining Vehicle Based on Improved Particle Swarm Optimization. Journal of Marine Science and Engineering, 11(9): 1797.
[45] Wang Y, Wang B, Li Z, et al., 2023, A Novel Particle Swarm Optimization Based on Hybrid-Learning Model. Mathematical Biosciences and Engineering, 20(4): 7056–7087.
[46] Zeng N, Wang Z, Liu W, et al., 2022, A Dynamic Neighborhood-Based Switching Particle Swarm Optimization Algorithm. IEEE Transactions on Cybernetics, 52(9): 9291–9301.
[47] Cui Q, Liu P, Du H, et al., 2023, Improved Multi-Objective Artificial Bee Colony Algorithm-Based Path Planning for Mobile Robots. Frontiers in Neurorobotics, 17: 1196683.
[48] Yu Z, Duan P, Meng L, et al., 2022, Multi-Objective Path Planning for Mobile Robot with an Improved Artificial Bee Colony Algorithm. Mathematical Biosciences and Engineering, 20(2): 2501–2529.
[49] Yildirim M, Akay R, 2025, An Efficient Grid-Based Path Planning Approach Using Improved Artificial Bee Colony Algorithm. Knowledge-Based Systems, 318: 113528.
[50] Ye F, Duan P, Meng L, et al., 2026, A Learning-Driven Artificial Bee Colony Algorithm for Mobile Robot Multi-Objective Path Planning. Applied Soft Computing, 192: 114692.
[51] Mirza N, 2020, Robotic Path Planning and Fuzzy Neural Networks. The International Arab Journal of Information Technology, 17(4A): 615–620.
[52] Chen S, Feng T, Li J, et al., 2025, Research on Intelligent Path Planning of Mobile Robot Based on Hybrid Symmetric Bio-Inspired Neural Network Algorithm in Complex Road Environments. Symmetry, 17(6): 836–863.
[53] Mulás-Tejeda E, Gómez-Espinosa A, Escobedo C, et al., 2024, Implementation of a Long Short-Term Memory Neural Network-Based Algorithm for Dynamic Obstacle Avoidance. Sensors, 24(10): 3004.
[54] Luo M, Hou X, Yang S, 2019, A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning. IEEE Access, 7: 142682–142691.
[55] Molina-Leal A, Gómez-Espinosa A, Escobedo C, et al., 2021, Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network. Applied Sciences, 11(22): 10689.
[56] Romano J, Le T, Fu W, et al., 2021, TPOT-NN: Augmenting Tree-Based Automated Machine Learning with Neural Network Estimators. Genetic Programming and Evolvable Machines, 22(2): 207–227.
[57] Yu J, Su Y, Liao Y, 2020, The Path Planning of Mobile Robot by Neural Networks and Hierarchical Reinforcement Learning. Frontiers in Neurorobotics, 14: 63.
[58] Yang Y, Juntao L, Lingling P, 2020, Multi-Robot Path Planning Based on a Deep Reinforcement Learning DQN Algorithm. CAAI Transactions on Intelligence Technology, 5(3): 177–183.
[59] Li H, Qi Y, 2020, A Robot Path Planning Method Based on Deep Reinforcement Learning in Complex Environments. Application Research of Computers, 37(S1): 129–131.
[60] Qi R, Wu X, 2022, Robot Path Planning Based on Deep Reinforcement Learning. Manufacturing Automation, 44(12): 177–180.
[61] Yuan G, Zhu B, Hu Y, et al., 2025, Assembly Measurement Path Planning for Mobile Robots Using an Improved Deep Reinforcement Learning. Applied Sciences, 15(23): 12406.
[62] Gök M, 2024, Dynamic Path Planning via Dueling Double Deep Q-Network (D3QN) With Prioritized Experience Replay. Applied Soft Computing, 158: 111503.
[63] Liu Y, Wang C, Wu H, et al., 2023, Mobile Robot Path Planning Based on Kinematically Constrained A-Star Algorithm and DWA Fusion Algorithm. Mathematics, 11(21): 4552.
[64] Zhang J, Ling H, Tang Z, et al., 2025, Path Planning of USV in Confined Waters Based on Improved A* and DWA Fusion Algorithm. Ocean Engineering, 322: 120475.
[65] Pan Y, Yang Y, Li W, 2021, A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV. IEEE Access, 9: 7994–8005.
[66] Benmachiche A, Derdour M, Kahil M, et al., 2025, Adaptive Hybrid PSO–APF Algorithm for Advanced Path Planning in Next-Generation Autonomous Robots. Sensors, 25(18): 5742.
