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Smooth Path Planning Method for Unmanned Surface Vessels Considering Environmental Disturbance

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  • Robot and Applications
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Abstract

To solve the problems of unsmooth path planning, insufficient dynamic obstacle avoidance ability, and environmental disturbance effect on the path planning result, this paper proposes a smooth path planning method for unmanned surface vessels (USVs) considering environmental disturbance. First, an improved A* algorithm, which uses the path smoothing method based on the minimum turning radius of a USV, is proposed for global path planning. The binary tree method is used instead of the enumeration method to select a relatively optimal path in the current situation to improve algorithm efficiency. In addition, the dynamic window approach (DWA) with the Convention on the International Regulation for Preventing Collision at Sea (COLREGs) constraints is used for local path planning. The dist function in the DWA algorithm is improved to enhance the DWA algorithm’s ability to avoid dynamic obstacles. Finally, the environmental disturbance function is derived and added to the A* and DWA algorithms to handle the effect of environmental disturbances, such as water flow, on the path planning result, which can significantly improve the path-planning ability of the algorithm in the presence of environmental disturbances. Simulation experiments are performed in three scenarios to verify the proposed algorithm. The experimental results show that compared with the other algorithms, the proposed algorithm can effectively avoid dynamic obstacles and reduce the impact of environmental disturbance on the path planning result. At the same time, the proposed algorithm has high efficiency and strong robustness.

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References

  1. A. Felski and K. Zwolak, “The ocean-going autonomous ship-challenges and threats,” Journal of Marine Science and Engineering, vol. 4, no. 41, pp. 1–16, 2020.

    Google Scholar 

  2. Z. Zhu, J. Xiao, and J. Q. Li, “Global path planning of wheeled robots using multi-objective memetic algorithms,” Integrated Computer Aided Engineering, vol. 22, no. 4, pp. 387–404, 2015.

    Article  Google Scholar 

  3. C. Altunbas, T. Alexeev, and M. Miften, “Effect of grid geometry on the transmission properties of 2D grids for flat detectors in CBCT,” Physics in Medicine and Biology, vol. 64, no. 22, pp. 225006–225006, 2019.

    Article  Google Scholar 

  4. Q. S. Zhang, X. Song, and Y. Yang, “Visual graph mining for graph matching,” Computer Vision and Image Understanding, vol. 178, no. 1, pp. 16–29, 2019.

    Article  Google Scholar 

  5. J. D. Zhang, Y. J. Feng, and F. F. Shi, “Vehicle routing in urban areas based on the oil consumption weight-Dijkstra algorithm,” IET Intell Transp, vol. 10, no. 7, pp. 495–502, 2016.

    Article  Google Scholar 

  6. D. S. Yershov and S. M. Lavalle, “Simplicial Dijkstra and A* Algorithms: From graphs to continuous spaces,” Advanced Robotics, vol. 26, no. 17, pp. 1–21, 2012.

    Article  Google Scholar 

  7. F. Kamil, T. Hong, and W. Khaksar, “An ANFIS-based optimized fuzzy-multilayer decision approach for a mobile robotic system in ever-changing environment,” International Journal of Control, Automation, and Systems, vol. 17, no. 1, pp. 253–266, 2019.

    Article  Google Scholar 

  8. P. Wang, S. Gao, and L. Li, “Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm,” Energies, vol. 12, no. 12, pp. 1–14, 2019.

    Article  Google Scholar 

  9. W. Youn, H. Ko, and H. Choi, “Collision-free autonomous navigation of a small UAV using low-cost sensors in GPS-denied environments,” International Journal of Control, Automation, and Systems, vol. 19, no. 3, pp. 1–16, 2020.

    Google Scholar 

  10. L. Heon-Cheol, Y. Touahmi, and L. Beom-Hee, “Grafting: A path replanning technique for rapidly-exploring random trees in dynamic environments,” Advanced Robotics, vol. 26, no. 18, pp. 2145–2168, 2012.

    Article  Google Scholar 

  11. J. Velagi, L. Vukovi, and B. Ibrahimovi, “Mobile robot motion framework based on enhanced robust panel method,” International Journal of Control, Automation, and Systems, vol. 18, no. 3, pp. 1264–1276, 2020.

    Article  Google Scholar 

  12. M. R. Zeng, L. Xi, and A. M. Xiao, “The free step length ant colony algorithm in mobile robot path planning,” Advanced Robotics, vol. 30, no. 23, pp. 1509–1514, 2016.

    Article  Google Scholar 

  13. M. Thi-Thoa, C. Cosmin, and T. Duc-Trung, “A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization,” Applied Soft Computing, vol. 59, no. 1, pp. 68–76, 2017.

    Google Scholar 

  14. Y. Wang, M. Yang, and G. Zhang, “Research on AGV path planning based on improved A star algorithm,” Fire Control & Command Control, vol. 46, no. 8, 2021.

    Google Scholar 

  15. B. Guo, Z. Kuang, J. H. Guan, M. T. Hu, L. X. Rao, and X. Q. Sun, “An improved A-star algorithm for complete coverage path planning of unmanned ships,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 36, no. 3, 2259009, 2022.

    Article  Google Scholar 

  16. M. Lan, H. Zhang, S. Meng, and J. Liu, “Volcanic ash region path planning based on improved A-star algorithm,” Journal of Advanced Transportation, vol. 2022 Article ID 9938975, 2022.

  17. Z. Jiang, W. Jun, and S. Xiao, “Autonomous land vehicle path planning algorithm based on improved heuristic function of A-star,” International Journal of Advanced Robotic Systems, vol. 18, no. 5, pp. 1–10, 2021.

    Google Scholar 

  18. J. Yu, J. Hou, and G. Chen, “Improved safety-first A-star algorithm for autonomous vehicles,” Proc. of the 5th International Conference on Advanced Robotics and Mechatronics (ICARM), 2020.

  19. Z. Lin, Z. J. Ying, and L. F. Yang, “Mobile robot path planning based on improved localized particle swarm optimization,” IEEE Sensors Journal, vol. 21, no. 5, pp. 6962–6972, 2021.

    Article  Google Scholar 

  20. Z. Liu, H. Liu, Z. Lu, and Q. Zheng, “A dynamic fusion pathfinding algorithm using delaunay triangulation and improved A-star for mobile robots,” IEEE Access, vol. 9, no. 9, pp. 20602–20621, 2021.

    Article  Google Scholar 

  21. Z. Hong, P. Sun, and X. Tong, H. Pan, R. Zhou, Y. Zhang, Y. Han, J. Wang, S. Yang, and L. Xu, “Improved A-star algorithm for long-distance off-road path planning using terrain data map,” ISPRS International Journal of Geo-Information, vol. 10, no. 11, pp. 785–801, 2021.

    Article  Google Scholar 

  22. Z. Zhang, J. Wu, J. Dai, and C. He, “Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment,” Proc. of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 1, no. 1, pp. 1–10, 2021.

    Article  Google Scholar 

  23. Y. Yang, Z. Lin, M. Yue, G. Chen, J. Sun, “Path planning of mobile robot with PSO-based APF and fuzzy-based DWA subject to moving obstacles,” Transactions of the Institute of Measurement and Control, vol. 44, no. 1, pp. 121–132, 2022.

    Article  Google Scholar 

  24. X. Bai, H. Jiang, J. Cui, K. Lu, P. Chen, M. Zhang, “UAV path planning based on improved A and DWA algorithms,” International Journal of Aerospace Engineering, 2021.

  25. W. Zhang, L. Shan, and L. Chang, “Distributed collision avoidance algorithm for multiple unmanned surface vessels based on improved DWA,” Control and Decision, 2022.

  26. Ø. Aleksander and G. Loe, “Collision avoidance for unmanned surface vehicles,” Department of Engineering Cybernetics, 2008.

  27. C. K. Tam and R. Bucknall, “Cooperative path planning algorithm for marine surface vessels,” Ocean Engineering, vol. 57, no. 1, pp. 25–33, 2013.

    Article  Google Scholar 

  28. Y. Chen, X. Kang, and P. Xu, “On algorithms for control of multiple mobile robots in complex environment,” Electronics Optics & Control, vol. 28, no. 4, pp. 48–52, 2021.

    Google Scholar 

  29. S. Liang, J. Liu, and X. Xian, “A Dynamic Window Approach to Collision Avoidance Considering Robot Size Con-straint,” Control Engineering of China, vol. 18, no. 6, pp. 872–876, 2011.

    Google Scholar 

  30. M. Missura and M. Bennewitz, “Predictive collision avoidance for the dynamic window approach,” Proc. of the 2019 International Conference on Robotics and Automation (ICRA), pp. 8620–8626, 2019.

  31. A. A. Pereira, J. Binney, and G. A. Hollinger, “Risk-aware path planning for autonomous under-water vehicles using predictive ocean models,” Field Robot, vol. 30, pp. 741–762, 2013.

    Article  Google Scholar 

  32. C. Petres, Y. Pailhas, P. Patron, Y. Petillot, J. Evans, and D. Lane, “Path planning for autonomous underwater vehicles,” IEEE Transactions on Robotics, vol. 23, no. 2, pp. 331–341, 2007.

    Article  Google Scholar 

  33. M. Soulignac, “Feasible and optimal path planning in strong current fields,” IEEE Transactions on Robotics, vol. 27, no. 1, pp. 89–98, 2011.

    Article  Google Scholar 

  34. W. Lan, “Path planning for underwater gliders in time-varying ocean current using deep reinforcement learning,” Ocean Engineering, vol. 262, 112226, 2022.

    Article  Google Scholar 

  35. Y. Sun, “Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy,” Ocean Engineering, vol. 259, 111907, 2022.

    Article  Google Scholar 

  36. A. Ammar, H. Bennaceur, I. Chari, A. Koubaa, and M. Alajlan, “Alajlan. Relaxed Dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments,” Soft Computing, vol. 20, no. 10, pp. 4149–4171, 2016.

    Article  Google Scholar 

  37. D. B. Bisandu, I. Moulitsas, and S. Filippone, “Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction,” Neural Computing and Applications, vol. 1, no. 1, pp. 1–26, 2022.

    Google Scholar 

  38. L. Liu, G. Han, Z. Xu, J. Jiang, and M. Martinez-Garcia, “Boundary Tracking of Continuous Objects Based on Binary Tree Structured SVM for Industrial Wireless Sensor Networks,” IEEE Transactions on Mobile Computing, vol. 21, no. 3, pp. 849–861, 2020.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Xiaoyi Wang, Yuting Bai, Jiguang Wu & Jiping Xu

  2. Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Xiaoyi Wang, Yuting Bai, Jiguang Wu & Jiping Xu

  3. China Food Flavor and Nutrition Health Innovation Center, Beijing Technology and Business University, Beijing Technology and Business University, Beijing, 100048, China

    Jiabin Yu, Zhihao Chen, Zhiyao Zhao, Xiaoyi Wang, Yuting Bai, Jiguang Wu & Jiping Xu

Authors
  1. Jiabin Yu
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Correspondence to Jiabin Yu.

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This work was supported in part by the National Natural Science Foundation of China (No.61903008), Beijing Talents Project (No.2020A28), Outstanding Youth Cultivation Project of Beijing Technology and Business University.

Jiabin Yu received his B.S. degree from the Beijing Technology and Business University, Beijing, China, in 2007, an M.S. degree in automation from the Beijing Institute of Technology, in 2009, and a Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, in 2012. He has been an Associate Professor with the Beijing Technology and Business University, since 2017. His current research interests cover water environment evaluation and prediction, motor control, and complex system design.

Zhihao Chen received his B.S. degree in electrical engineering and automation from the Beijing Technology and Business University, Beijing, China, in 2021, where he is currently pursuing a master’s degree. His current research interests include path planning of mobile equipment and research on path planning algorithm.

Zhiyao Zhao received his B.S. degree in automation from the Beijing Technology and Business University, Beijing, China, in 2011, and a Ph.D. degree in guidance, navigation, and control from the School of Automation Science and Electrical Engineering, Beihang University, Beijing, in 2017. He has been a Lecturer with the Beijing Technology and Business University, since 2017. His current research interests include water environment evaluation and prediction, system health management, and stochastic hybrid systems.

Xiaoyi Wang received his B.S. degree in automation from the Department of Automation, Shenyang College of Technology, Shenyang, China, in 2000, an M.S. degree in optics from Shanxi University, Shanxi, China, in 2003, and a Ph.D. degree in control theory and control engineering from the School of Automation, Beijing Institute of Technology, Beijing, China, in 2006. He has been a Professor with the Beijing Technology and Business University, since 2013. His current research interests include water environment modeling, optimization and decision-making, and optimal control.

Yuting Bai received his Ph.D. degree in control science and engineering from Beijing Institute of Technology, an M.S. degree in management science and engineering from Beijing Technology and Business University, and a B.S. degree in automation from Beijing Technology and Business University. He is now a lecturer in Beijing Technology and Business University. His research mainly covers state estimation, information fusion, and machine learning.

Jiguang Wu received his B.S. degree in electrical engineering and automation from the Beijing Technology and Business University, Beijing, China, in 2021, where he is currently pursuing a master’s degree. His current research interest includes mobile robot.

Jiping Xu received his B.S. and M.S. degrees in automation from the Beijing Technology and Business University, Beijing, China, in 2002 and 2005, respectively, and a Ph.D. degree in control theory and control engineering from the School of Automation, Beijing Institute of Technology, Beijing, in 2010. He has been an Associate Professor with the Beijing Technology and Business University, since 2010. His current research interests include water environment evaluation and prediction, and big data analysis.

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Yu, J., Chen, Z., Zhao, Z. et al. Smooth Path Planning Method for Unmanned Surface Vessels Considering Environmental Disturbance. Int. J. Control Autom. Syst. 21, 3285–3298 (2023). https://doi.org/10.1007/s12555-022-0826-9

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  • DOI: https://doi.org/10.1007/s12555-022-0826-9

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