Fermat Curve Path Planning Method for Ship Trajectory Tracking
Abstract
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Abstract: Traditional ship motion systems are no longer sufficient to meet the navigation demands of vessels. Therefore, a path planning method based on Fermat curves is proposed in this study. Within the ship operation system, Fermat curves are employed to enhance straight-line paths, achieving smooth motion between waypoints. The Fermat curve path planning method is integrated into ship guidance algorithms to guide vessel headings. The performance of the proposed guidance algorithm is evaluated, and experimental results indicate that the algorithm can maintain a high similarity between actual and desired trajectories. In comparison to traditional Line-of-sight methods, the proposed algorithm demonstrates shorter path planning lengths. Additionally, the algorithm's heading angles can compensate for sideslip angles in perturbed environments. Comparative experiments with other guidance algorithms reveal that the proposed algorithm has the shortest runtime, saving more energy. Furthermore, it achieves accuracy and precision rates of 87.1% and 91.3%, respectively, surpassing other algorithms and providing high-precision guidance for vessels. In terms of error comparison, the proposed algorithm keeps guidance errors around 2, showcasing superior performance and feasibility in ship path guidance.
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Huo L. The global path planning for vehicular communication using ant colony algorithm in emerging wireless cloud computing. Wireless Networks, 2023, 29(2): 833-842.
Yu W, Sun H, Feng T, Lv Y, Guo X, Xin G. A novel reliable path planning approach for multimodal networks based on a two-factor bound convergence algorithm. Modern Physics Letters B, 2022, 36(23): 2250007-2250042.
Zou A, Wang L, Li W, Cai J, Wang H, TanT. Mobile robot path planning using improved mayfly optimization algorithm and dynamic window approach. The Journal of Supercomputing, 2023, 79(8): 8340-8367.
Wang X, Lyu Z, Wei Z, Wang L, Lu Y, Shi L. Multi-objective path planning algorithm for mobile charger in wireless rechargeable sensor networks. Wireless Networks, 2023, 29(1): 267-283.
Williams A. Human-Centric Functional Computing as an Approach to Human-Like Computation//Artificial Intelligence and Applications. 2023, 1(2): 118-137.
Li Y, Aghvami A H, Dong D. Path planning for cellular-connected UAV: A DRL solution with quantum-inspired experience replay. IEEE Transactions on Wireless Communications, 2022, 21(10): 7897-7912.
Meng H, Zhang H. Mobile Robot Path Planning Method Based on Deep Reinforcement Learning Algorithm. Journal of Circuits, Systems and Computers, 2022, 31(15): 85-103.
Rath A K, Parhi D R, Das H C, Kumar P B, Mahto M K. Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot. International Journal of Intelligent Unmanned Systems, 2021, 9(3): 169-177.
García E, Villar J R, Tan Q, Sedano J, Chira Cl. An efficient multi-robot path planning solution using A* and coevolutionary algorithms. Integrated Computer-Aided Engineering, 2023, 30(1): 41-52.
Li Y, Huang Y, Ge L, LI X. Mobile Robot Path Planning Based on Angle-Guided Ant Colony Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 2022, 13(1): 1-19.
Shiqian L, Yuanjun S. Underactuated Stratospheric Airship Trajectory Control Using an Adaptive Integral Backstepping Approach. Journal of Aircraft, 2018, 55(6):1-15.
Telen S, Barel M V, Verschelde J. A Robust Numerical Path Tracking Algorithm for Polynomial Homotopy Continuation. SIAM Journal on Scientific Computing, 2020, 6(42): 3610-3637.
Bai Y, Lv J, Wang C, Geng Y. Ship tracking method for resisting similar shape information under satellite videos. Journal of Applied Remote Sensing, 2022, 16(2): 26517-026534.
Wu J, Cao C, Zhou Y, Zeng X, Feng Z, Wu Q, Huang Z. Multiple ship tracking in remote sensing images using deep learning. Remote Sensing, 2021, 13(18): 3601-3601.
Strub M P, Gammell J D. Adaptively informed trees (AIT*) and effort informed trees (EIT*): Asymmetric bidirectional sampling-based path planning. The International Journal of Robotics Research, 2022, 41(4): 390-417.
Changizi A, Emadi M J. Age‐optimal path planning for finite‐battery UAV‐assisted data dissemination in IoT networks. IET Communications, 2021, 15(10): 1287-1296.
Parmar K, Guzzetti D. Interactive imitation learning for spacecraft path-planning in binary asteroid systems. Advances in Space Research, 2021, 68(4): 1928-1951.
Liu A, Jiang J. Solving path planning problem based on logistic beetle algorithm search–pigeon-inspired optimisation algorithm. Electronics Letters, 2020, 56(21): 1105-1108.
Lou W, Zhu M, Guo X, Liang, H. Command filtered sliding mode trajectory tracking control for unmanned airships based on RBFNN approximation. Advances in Space Research, 2019, 63(3): 1111-1121.
Wang B D, Song J, Li R, Han R, Zheng G, Yang H. A novel particle tracking velocimetry method for complex granular flow field. Chinese Physics B, 2020, 29(1): 14207-14207.
DOI: https://doi.org/10.31449/inf.v48i8.5735
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