International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 4, 8 Aug. 2025
Cover page and Table of Contents: PDF (size: 1503KB)
An Enhanced Adaptive B-spline Smoothing Approach for UAV Path Planning
PDF (1503KB), PP.1-13
Views: 0 Downloads: 0
Author(s)
Index Terms
UAV, Path Planning, B-spline, 3D Environment, Optimization
Abstract
This paper presents an Enhanced Adaptive B-Spline Smoothing approach for UAV path planning in complex three-dimensional environments. By leveraging the inherent local control and smoothness properties of cubic B-Splines, the proposed method integrates an adaptive knot selection mechanism—optimized via a genetic algorithm—with curvature-aware control point refinement to generate dynamically feasible and smooth flight paths. Simulation studies in a cluttered 3D airspace show that the proposed technique reduces path length and lowers maximum curvature compared to uniform and chord-length-based B-Spline strategies. Despite a moderate computational overhead, the results demonstrate smoother, more stable flight trajectories that adhere to aerodynamic constraints and ensure safe obstacle avoidance. This approach is particularly valuable for near-real-time missions, where flight stability, rapid re-planning, and energy efficiency are paramount. Results emphasize the potential of the proposed method for improving UAV navigation in various applications—such as urban logistics, infrastructure inspection, and search-and-rescue—by providing better maneuverability, reduced energy consumption, and increased operational safety to the UAV agents.
Cite This Paper
Mykola Nikolaiev, Mykhailo Novotarskyi, "An Enhanced Adaptive B-spline Smoothing Approach for UAV Path Planning", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.4, pp.1-13, 2025. DOI:10.5815/ijisa.2025.04.01
Reference
[1]T. Wu, Z. Zhang, F. Jing, and M. Gao, “A Dynamic Path Planning Method for UAVs Based on Improved Informed-RRT* Fused Dynamic Windows,” Drones, vol. 8, no. 10, p. 539, Oct. 2024, doi: 10.3390/drones8100539.
[2]A. Li, J. Cao, S. Li, Z. Huang, J. Wang, and G. Liu, “Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion,” Applied Sciences, vol. 12, no. 6, p. 2913, Mar. 2022, doi: 10.3390/app12062913.
[3]M. Wicaksono and S. Y. Shin, “Optimizing UAV Navigation through Non-Uniform B-Spline Trajectory for Tracking UAV Enemy,” 2023 14th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp. 1075–1078, Oct. 11, 2023. doi: 10.1109/ictc58733.2023.10392619.
[4]Z. Cui and Y. Wang, “UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique,” IEEE Access, vol. 9, pp. 59486–59497, 2021, doi: 10.1109/access.2021.3073704.
[5]W. Ding, W. Gao, K. Wang, and S. Shen, “An Efficient B-Spline-Based Kinodynamic Replanning Framework for Quadrotors,” IEEE Trans. Robot., vol. 35, no. 6, pp. 1287–1306, Dec. 2019, doi: 10.1109/tro.2019.2926390.
[6]G. Airlangga et al., “Adaptive Path Planning for Multi-UAV Systems in Dynamic 3D Environments: A Multi-Objective Framework,” Designs, vol. 8, no. 6, p. 136, Dec. 2024, doi: 10.3390/designs8060136.
[7]W. Sun, P. Sun, W. Ding, J. Zhao, and Y. Li, “Gradient-based autonomous obstacle avoidance trajectory planning for B-spline UAVs,” Sci Rep, vol. 14, no. 1, Jun. 2024, doi: 10.1038/s41598-024-65463-w.
[8]Z. Xu, Y. Xiu, X. Zhan, B. Chen, and K. Shimada, “Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization,” arXiv, 2022, doi: 10.48550/ARXIV.2209.07003.
[9]A. Haidar Ahmad, O. Zahwe, A. Nasser, and B. Clement, “Path Planning for Unmanned Aerial Vehicles in Dynamic Environments: A Novel Approach Using Improved A* and Grey Wolf Optimizer,” WEVJ, vol. 15, no. 11, p. 531, Nov. 2024, doi: 10.3390/wevj15110531.
[10]J. Luo, Y. Tian, and Z. Wang, “Research on Unmanned Aerial Vehicle Path Planning,” Drones, vol. 8, no. 2, p. 51, Feb. 2024, doi: 10.3390/drones8020051.
[11]Y. Alqudsi and M. Makaraci, “UAV swarms: research, challenges, and future directions,” J. Eng. Appl. Sci., vol. 72, no. 1, Jan. 2025, doi: 10.1186/s44147-025-00582-3.
[12]G. Ahmed and T. Sheltami, “Novel Energy-Aware 3D UAV Path Planning and Collision Avoidance Using Receding Horizon and Optimization-Based Control,” Drones, vol. 8, no. 11, p. 682, Nov. 2024, doi: 10.3390/drones8110682.
[13]J. Choi, H. Chin, H. Park, D. Kwon, D. Baek, and S.-H. Lee, “Safe and Efficient Trajectory Optimization for Autonomous Vehicles Using B-Spline With Incremental Path Flattening,” IEEE Trans. Intell. Transport. Syst., vol. 26, no. 2, pp. 1797–1811, Feb. 2025, doi: 10.1109/tits.2024.3493060.
[14]R. Zhang, S. Li, Y. Ding, X. Qin, and Q. Xia, “UAV Path Planning Algorithm Based on Improved Harris Hawks Optimization,” Sensors, vol. 22, no. 14, p. 5232, Jul. 2022, doi: 10.3390/s22145232.
[15]“Can Autonomous UAVs Help in Search and Rescue Missions?,” Orbitshub, Feb. 2025. [Online]. Available: https://orbitshub.com/autonomous-uavs-search-rescue/. [Accessed: Jun. 1, 2025].
[16]J. Tang, Y. Liang, and K. Li, “Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning,” Drones, vol. 8, no. 2, p. 60, Feb. 2024, doi: 10.3390/drones8020060.
[17]B. Liu, G. Xu, G. Xu, C. Wang, and P. Zuo, “Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET,” Sensors, vol. 23, no. 3, p. 1204, Jan. 2023, doi: 10.3390/s23031204.
[18]M. Gao and X. Zhang, “UAV path planning with kinematic constraints based on deep reinforcement learning,” 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022). SPIE, p. 75, Aug. 01, 2022. doi: 10.1117/12.2640186.
[19]J. Li and Y. Liu, “Deep Reinforcement Learning based Adaptive Real-Time Path Planning for UAV,” 2021 8th International Conference on Dependable Systems and Their Applications (DSA). IEEE, pp. 522–530, Aug. 2021. doi: 10.1109/dsa52907.2021.00077.
[20]W. Yun, Z. Lu, P. He, X. Jiang, and Y. Dai, “Parameter global reliability sensitivity analysis with meta-models: A probability estimation-driven approach,” Aerospace Science and Technology, vol. 106, p. 106040, Nov. 2020, doi: 10.1016/j.ast.2020.106040.
[21]R. Chai, Y. Gao, R. Sun, L. Zhao, and Q. Chen, “Time-Oriented Joint Clustering and UAV Trajectory Planning in UAV-Assisted WSNs: Leveraging Parallel Transmission and Variable Velocity Scheme,” IEEE Trans. Intell. Transport. Syst., vol. 24, no. 11, pp. 12092–12106, Nov. 2023, doi: 10.1109/tits.2023.3299842.
[22]Q. Wu, Y. Zeng, and R. Zhang, “Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks,” IEEE Trans. Wireless Commun., vol. 17, no. 3, pp. 2109–2121, Mar. 2018, doi: 10.1109/twc.2017.2789293.
[23]W. Wang, J. Zhao, Z. Li, and J. Huang, “Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm,” Journal of Robotics, vol. 2021, pp. 1–10, Sep. 2021, doi: 10.1155/2021/4109821.
[24]B. Wang, T. Wang, Z. Li, L. Chen, and D. Chen, “An adaptive look-ahead position and orientation interpolation algorithm for multi-path segments smooth transition,” Control and Decision, vol. 34, no. 6, pp. 1211–1218, Jun. 2019, doi: 10.13195/j.kzyjc.2017.1594.
[25]M. Elbanhawi, M. Simic, and R. N. Jazar, “Continuous Path Smoothing for Car-Like Robots Using B-Spline Curves,” J Intell Robot Syst, vol. 80, no. S1, pp. 23–56, Jan. 2015, doi: 10.1007/s10846-014-0172-0.
[26]L. Zhao, G. Li, X. Guo, S. Li, Y. Wen, and Q. Ye, “Time-optimal trajectory planning of permanent magnet spherical motor based on genetic algorithm,” 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp. 828–833, Jun. 2017. doi: 10.1109/iciea.2017.8282954.
[27]Y. Alqudsi, M. Makaraci, A. Kassem, and G. El-Bayoumi, “A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs,” Robotics and Autonomous Systems, vol. 170, p. 104532, Dec. 2023, doi: 10.1016/j.robot.2023.104532.