Research Paper:
Path Planning Based on Improved Bidirectional A* Algorithm
Jian Zhang*, Hui Wang*,, and Shaobao Wu**
*Minnan Normal University
No.36 Xianqianzhi Street, Xiangcheng District, Zhangzhou 363000, China
Corresponding author
**Liming Vocational University
No.298 Tonggang West Street, Fengze District, Quanzhou 362000, China
With the rapid expansion of warehouse scale, traditional path planning algorithms suffer from critical limitations in computational efficiency and path smoothness within complex dynamic environments. This work proposes an enhanced bidirectional A* algorithm integrating a discarded domain search strategy to dynamically optimize node expansion scope, effectively reducing computational redundancy by 20%. Combined with Gaussian filtering for eliminating sharp path discontinuities, the method significantly enhances operational stability. Experimental results demonstrate that compared to conventional bidirectional A* algorithms, the improved approach achieves a 20% reduction in path search time while substantially improving path smoothness. These findings provide an efficient and reliable solution for intelligent warehouse navigation systems. Future work will focus on implementing this methodology in dynamic environments and practical warehousing applications.
- [1] A. Ham, “Drone-Based Material Transfer System in a Robotic Mobile Fulfillment Center,” IEEE Trans. on Automation Science and Engineering, Vol.17, Issue 2, pp. 957-965, 2019. https://doi.org/10.1109/TASE.2019.2952523
- [2] Z. Liu, H. Wang, W. Chen, J. Yu, and J. Chen, “An Incidental Delivery Based Method for Resolving Multirobot Pairwised Transportation Problems,” IEEE Trans. on Intelligent Transportation Systems, Vol.17, Issue 7, pp. 1852-1866, 2016. https://doi.org/10.1109/TITS.2015.2508783
- [3] H. Ma, W. Hönig, L. Cohen, T. Uras, H. Xu, T. K. S. Kumar, N. Ayanian, and S. Koenig, “Overview: A Hierarchical Framework for Plan Generation and Execution in Multirobot Systems,” IEEE Intelligent Systems, Vol.32, Issue 6, pp. 6-12, 2017. https://doi.org/10.1109/MIS.2017.4531217
- [4] R. Tai, J. Wang, and W. Chen, “A prioritized planning algorithm of trajectory coordination based on time windows for multiple AGVs with delay disturbance,” Assembly Automation, Vol.39, Issue 5, pp. 753-768, 2019. https://doi.org/10.1108/AA-03-2019-0054
- [5] A. Ravankar, A. A. Ravankar, Y. Kobayashi, Y. Hoshino, and C.-C. Peng, “Path Smoothing Techniques in Robot Navigation: State-of-the-Art, Current and Future Challenges,” Sensors, Vol.18, Issue 9, Article No.3170, 2018. https://doi.org/10.3390/s18093170
- [6] R. Song, Y. Liu, and R. Bucknall, “Smoothed A* algorithm for practical unmanned surface vehicle path planning,” Applied Ocean Research, Vol.83, pp. 9-20, 2019. https://doi.org/10.1016/j.apor.2018.12.001
- [7] G. Li and W. Chou, “Path planning for mobile robot using self-adaptive learning particle swarm optimization,” Science China Information Sciences, Vol.61, Article No.052204, 2018. https://doi.org/10.1007/s11432-016-9115-2
- [8] C. Wang, L. Wang, J. Qin, Z. Wu, L. Duan, Z. Li, M. Cao, X. Ou, X. Su, W. Li et al., “Path planning of automated guided vehicles based on improved A-Star algorithm,” 2015 IEEE Int. Conf. on Information and Automation, pp. 2071-2076, 2015. https://doi.org/10.1109/ICInfA.2015.7279630
- [9] H. S. Hewawasam, M. Y. Ibrahim, and G. K. Appuhamillage, “Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments,” IEEE Open J. of the Industrial Electronics Society, Vol.3, pp. 353-365, 2022. https://doi.org/10.1109/OJIES.2022.3179617
- [10] K. Karur, N. Sharma, C. Dharmatti, and J. E. Siegel, “A Survey of Path Planning Algorithms for Mobile Robots,” Vehicles, Vol.3, Issue 3, pp. 448-468, 2021. https://doi.org/10.3390/vehicles3030027
- [11] J. Guo, L. Liu, Q. Liu, and Y. Qu, “An Improvement of D* Algorithm for Mobile Robot Path Planning in Partial Unknown Environment,” 2009 2nd Int. Conf. on Intelligent Computation Technology and Automation, Vol.3, pp. 394-397, 2009. https://doi.org/10.1109/ICICTA.2009.561
- [12] L. Zhang, Z. Lin, J. Wang, and B. He, “Rapidly-exploring random trees multi-robot map exploration under optimization framework,” Robotics and Autonomous Systems, Vol.131, Article No.103565, 2020. https://doi.org/10.1016/j.robot.2020.103565
- [13] I. Saleh, N. Borhan, and W. Rahiman, “Smoothing RRT Path for Mobile Robot Navigation Using Bio-inspired Optimization Method,” Pertanika J. of Science & Technology, Vol.32, Issue 5, Article No.JST-4891-2023, 2024. https://doi.org/10.47836/pjst.32.5.22
- [14] M. A. Awadallah, S. N. Makhadmeh, M. A. Al-Betar, L. M. Dalbah, A. Al-Redhaei, S. Kouka, and O. S. Enshassi, “Multi-objective Ant Colony Optimization: Review,” Archives of Computational Methods in Engineering, Vol.32, pp. 995-1037, 2024. https://doi.org/10.1007/s11831-024-10178-4
- [15] J. Tang, G. Liu, and Q. Pan, “A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends,” IEEE/CAA J. of Automatica Sinica, Vol.8, Issue 10, pp. 1627-1643, 2021. https://doi.org/10.1109/JAS.2021.1004129
- [16] Y. Song, Y. Wu, Y. Guo, R. Yan, P. N. Suganthan, Y. Zhang, W. Pedrycz, S. Das, R. Mallipeddi, O. S. Ajani, and Q. Feng, “Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities,” Swarm and Evolutionary Computation, Vol.86, Article No.101517, 2024. https://doi.org/10.1016/j.swevo.2024.101517
- [17] Q. Luo, H. Wang, Y. Zheng, and J. He, “Research on path planning of mobile robot based on improved ant colony algorithm,” Neural Computing and Applications, Vol.32, pp. 1555-1566, 2020. https://doi.org/10.1007/s00521-019-04172-2
- [18] 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, Article No.119410, 2023. https://doi.org/10.1016/j.eswa.2022.119410
- [19] H. Yang, J. Qi, Y. Miao, H. Sun, and J. Li, “A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization,” IEEE Trans. on Industrial Electronics, Vol.66, Issue 11, pp. 8557-8566, 2018. https://doi.org/10.1109/TIE.2018.2886798
- [20] L. Nardi and C. Stachniss, “Actively Improving Robot Navigation on Different Terrains Using Gaussian Process Mixture Models,” 2019 Int. Conf. on Robotics and Automation (ICRA), pp. 4104-4110, 2019. https://doi.org/10.1109/ICRA.2019.8794079
- [21] P. K. Mohanty and D. R. Parhi, “Controlling the Motion of an Autonomous Mobile Robot Using Various Techniques: A Review,” J. of Advance Mechanical Engineering, Vol.1, No.1, pp. 24-39, 2013.
- [22] J. Kong, P. Zhang, and X. Liu, “Research on Improved A* Algorithm of Bidirectional Search Mechanism,” Comput. Eng. Appl., Vol.57, No.8, pp. 231-237, 2021.
- [23] C. Scheiderer, T. Thun, and T. Meisen, “Bézier Curve Based Continuous and Smooth Motion Planning for Self-Learning Industrial Robots,” Procedia Manufacturing, Vol.38, pp. 423-430, 2019. https://doi.org/10.1016/j.promfg.2020.01.054
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