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A path planning algorithm for unmanned surface vessel with pose constraints in an unknown environment

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초록

To solve the problem of full-process path planning that includes multi-target path planning and automatic mooring, this paper proposes a hybrid D*Lite algorithm for an unmanned surface vessel with pose constraints (position and attitude) in an unknown environment. First, to solve the problem of multi-target path planning under the pose constraints, the D*Lite algorithm is combined with the Dubins search tree algorithm, and the corner at the sampling point is eliminated so that the path is smoothly connected. Second, to achieve the automatic mooring under the pose constraints and the avoidance of unknown obstacles, the D*Lite algorithm is combined with Reeds-Shepp curves to introduce the heading angle into the traditional two-dimensional search. This paper has conducted three groups of simulation experiments. In simulation experiment 1 and 2, compared with other algorithms in the multi-target points and automatic mooring environment for simulation experiments respectively, the D*Lite (Dubins) and D*Lite (RS) algorithm outperformed other algorithms in terms of planning time, planning distance, and smoothness. In simulation experiment 3, the effectiveness and superiority of the proposed method were verified on the grid map and compared with the traditional D*Lite algorithm. A visualization simulation was conducted, and the results revealed that the hybrid D*Lite algorithm enabled the unmanned surface vessel to efficiently achieve smooth movement with full-process throughout unknown environment.

Abstract

To solve the problem of full-process path planning that includes multi-target path planning and automatic mooring, this paper proposes a hybrid D*Lite algorithm for an unmanned surface vessel with pose constraints (position and attitude) in an unknown environment. First, to solve the problem of multi-target path planning under the pose constraints, the D*Lite algorithm is combined with the Dubins search tree algorithm, and the corner at the sampling point is eliminated so that the path is smoothly connected. Second, to achieve the automatic mooring under the pose constraints and the avoidance of unknown obstacles, the D*Lite algorithm is combined with Reeds-Shepp curves to introduce the heading angle into the traditional two-dimensional search. This paper has conducted three groups of simulation experiments. In simulation experiment 1 and 2, compared with other algorithms in the multi-target points and automatic mooring environment for simulation experiments respectively, the D*Lite (Dubins) and D*Lite (RS) algorithm outperformed other algorithms in terms of planning time, planning distance, and smoothness. In simulation experiment 3, the effectiveness and superiority of the proposed method were verified on the grid map and compared with the traditional D*Lite algorithm. A visualization simulation was conducted, and the results revealed that the hybrid D*Lite algorithm enabled the unmanned surface vessel to efficiently achieve smooth movement with full-process throughout unknown environment.

참고문헌 (37)

  1. Al-Mutib, K., "D* lite based real-time multi-agent path planning in dynamic environments", 2011 Third International Conference on Computational Intelligence, Modelling & Simulation, pp.170~174 (2011)
  2. Bashiri, M., "Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm", Appl. Math. Model, 54, pp.151~169 (2018)
  3. Chen, Z., "Adescent method for the dubins traveling salesman problem with neighborhoods", Front. Inf. Technol. Electron. Eng, 22, pp.732~740 (2021)
  4. Chen, Z., "A hybrid path planning method based on articulated vehicle model", Cmc-Comput. Mater. Continua, 65, pp.1781~1793 (2020)
  5. Cheng, C., "Path planning and obstacle avoidance for auv : A review", Ocean Eng, 235, pp.109355~ (2021)
  6. Chun, D. H., "Method for collision avoidance based on deep reinforcement learning with path-speed control for an autonomous ship", Int. J. Nav. Archit. Ocean Eng, 16, pp.100579~ (2024)
  7. Cil, Z. A., "Robotic disassembly line balancing problem : A mathematical model and ant colony optimization approach", Appl. Math. Model, 86, pp.335~348 (2020)
  8. Cui, P., "Smooth path planning for robot docking in unknown environment with obstacles" (2018), Complaxity
  9. Dolgov, D., "Path planning for autonomous vehicles in unknown semi-structured environments", Int. J. Robot. Res, 29, pp.485~501 (2010)
  10. Ferguson, D., "Robotics Research", pp.239~253 (2007), Springer
  11. Filaretov, V. F., "Planning smooth paths for mobile robots in an unknown environment", J. Comput. Syst. Sci. Int, 56, pp.738~748 (2017)
  12. Han, L., "Unified path planner for parking an autonomous vehicle based on rrt", 2011 IEEE International Conference on Robotics and Automation (2011), ICRA, IEEE
  13. Janos, J., "Multi-goal path planning using multiple random trees", IEEE Robot. Autom. Lett, 6, pp.4201~4208 (2021)
  14. Jose, S., "Mobile robot remote path planning and motion control in a maze environment", Proceedings of 2nd IEEE International Conference on Engineering & Technology. ICETECH-2016, pp.207~209 (2016), IEEE
  15. Koenig, S., "Fast replanning for navigation in unknown terrain", IEEE Trans. Robot, 21, pp.354~363 (2005)
  16. Li, H., "Forward search optimization and subgoal-based hybrid path planning to shorten and smooth global path for mobile robots", Knowl.-Based Syst, pp.258~ (2022)
  17. Lin, N., "A novel improved bat algorithm in uav path planning", Cmc-Comput. Mater. Continua, 61, pp.323~344 (2019)
  18. Liu, C., "An improved a-star algorithm considering water current, traffic separation and berthing for vessel path planning", Appl. Sci.-Basel, 9 (2019)
  19. Luviano Cruz, D., "Path planning of multi-agent systems in unknown environment with neural kernel smoothing and reinforcement learning", Neurocomputing, 233, pp.34~42 (2017)
  20. Mansour, K. A., "D* lite based realtime multi-agent path planning in dynamic environments", Third International Conference on Computational Intelligence (2011)
  21. Mohanty, P. K., "Optimal path planning for a mobile robot using cuckoo search algorithm", J. Exp. Theor. Artif. Intell, 28, pp.35~52 (2016)
  22. Niu, H., "Efficient path planning algorithms for unmanned surface vehicle", Ifca Pap, 49, pp.121~126 (2016)
  23. Park, J., "Mission planning and performance verification of an unmanned surface vehicle using a genetic algorithm", Int. J. Nav. Archit. Ocean Eng, 13, pp.575~584 (2021)
  24. Ren, Z., "Multi-objective path-based d* lite", IEEE Robot. Autom. Lett, 7, pp.3318~3325 (2022)
  25. Saranrittichai, P., "Robust local obstacle avoidance for mobile robot based on dynamic window approach", 10th International Conference on Electrical Engineering, 2013, ~
  26. Savla, K., "On the point-to-point and traveling salesperson problems for Dubins’ vehicle", ACC: Proceedings of the 2005 American Control Conference, 1–7, pp.786~791 (2005), IEEE
  27. Seo, C., "Ship collision avoidance route planning using cri-based a*algorithm", Int. J. Nav. Archit. Ocean Eng, 15, pp.100551~ (2023)
  28. Song, B., "On global smooth path planning for mobile robots using a novel multimodal delayed pso algorithm", Cogn. Comput, 9, pp.5~17 (2017)
  29. Sun, B., "Three dimensional D* lite path planning for autonomous underwater vehicle under partly unknown environment", Proceedings of the 2016 12th World Congress on Intelligent Control and Automation, pp.3248~3252 (2016), WCICA, IEEE
  30. Wang, J., "An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle", Int. J. Nav. Archit. Ocean Eng, 15, pp.100528~ (2023)
  31. Xiang, C., "The path planning study of multi-task logistics uavs under complex low airspace", Proceedings of the 33rd Chinese Control and Decision Conference. CCDC 2021, pp.5238~5242 (2021), IEEE
  32. Yoo, Y., "Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches", Knowl. -Based Syst, 178, pp.74~83 (2019)
  33. Yu, D., "Method for anti-collision path planning using velocity obstacle and a* algorithms for maritime autonomous surface ship", Int. J. Nav. Archit. Ocean Eng, pp.100586~ (2024)
  34. Yu, J., "Agv multi-objective path planning method based on improved cuckoo algorithm", 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (2019), IAEAC
  35. Yu, J., "Path planning of unmanned surface vessel in an unknown environment based on improved D*lite algorithm", Ocean Eng, pp.266~ (2022)
  36. Yujie, L., "Ship path planning based on improved particle swarm optimization", 2018 Chinese Automation Congress, pp.226~230 (2018), CAC
  37. Zips, P., "A fast motion planning algorithm for car parking based on static optimization", 2013 IEEE, 2013, 2392~2397[DOI]

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