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Online Heuristically Planning for Relative Optimal Paths Using a Stochastic Algorithm for USVs

  • Naifeng Wen (a1) (a2), Rubo Zhang (a1) (a2), Guanqun Liu (a1) (a2) and Junwei Wu (a1) (a2)

Abstract

This paper attempts to solve a challenge in online relative optimal path planning of unmanned surface vehicles (USVs) caused by current and wave disturbance in the practical marine environment. The asymptotically optimal rapidly extending random tree (RRT*) method for local path optimisation is improved. Based on that, an online path planning (OPP) scheme is proposed according to the USV's kinematic and dynamic model. The execution efficiency of RRT* is improved by reduction of the sampling space that is used for randomly learning environmental knowledge. A heuristic sampling scheme is proposed based on the proportional navigation guidance (PNG) method that is used to enable the OPP procedure to utilise the reference information of the global path. Meanwhile, PNG is used to guide RRT* in generating feasible paths with a small amount of gentle turns. The dynamic obstacle avoidance problem is also investigated based on the International Regulations for Preventing Collisions at Sea. Case studies demonstrate that the proposed method efficiently plans paths that are relatively easier to execute and lower in fuel expenditure than traditional schemes. The dynamic obstacle avoidance ability of the proposed scheme is also attested.

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Aguiar, A. P., Almeida, J., Bayat, M., Cardeira, B., Cunha, R., Häusler, A., Maurya, P., Oliveira, A., Pascoal, A. and Pereira, A. (2009). Cooperative control of multiple marine vehicles: theoretical challenges and practical issues. IFAC Proceedings Volumes, 42, 412417.
Beard, R. W., McLain, T. W., Nelson, D. B., Kingston, D. and Johanson, D. (2006). Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proceedings of the IEEE, 94, 13061324.
Bibuli, M., Bruzzone, G., Caccia, M., Indiveri, G. and Zizzari, A. A. (2008). Line Following Guidance Control: Application to the Charlie Unmanned Surface Vehicle. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, 3641–3646.
Bibuli, M., Bruzzone, G., Caccia, M. and Lapierre, L. (2009). Path-following algorithms and experiments for an unmanned surface vehicle. Journal of Field Robotics, 26, 669688.
Bibuli, M., Caharija, W., Pettersen, K. Y., Bruzzone, G., Caccia, M. and Zereik, E. (2014). ILOS guidance – experiments and tuning. IFAC Proceedings Volumes, 47, 42094214.
Bibuli, M., Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A. (2018). A two layered optimal approach towards cooperative motion planning of unmanned surface vehicles in a constrained maritime environment. IFAC-PapersOnLine, 51, 378383.
Breivik, M., Hovstein, V. E. and Fossen, T. I. (2008). Straight-line target tracking for unmanned surface vehicles. Modeling, Identification and Control, 29(4), 131149.
Caccia, M., Bibuli, M., Bono, R., Bruzzone, G., Bruzzone, G. and Spirandelli, E. (2007). Unmanned surface vehicle for coastal and protected waters applications: the Charlie project. Marine Technology Society Journal, 41, 6271.
Caccia, M., Bibuli, M., Bono, R. and Bruzzone, G. (2008). Basic navigation, guidance and control of an unmanned surface vehicle. Autonomous Robots, 25, 349365.
Campbell, S. and Naeem, W. (2012). A rule-based heuristic method for Colregs-compliant collision avoidance for an unmanned surface vehicle. IFAC Proceedings Volumes, 45, 386391.
Canny, J. and Reif, J. (1987). New Lower Bound Techniques for Robot Motion Planning Problems. 28th Annual Symposium on Foundations of Computer Science (sfcs 1987), Los Angeles, CA, USA, 49–60.
Casalino, G., Turetta, A. and Simetti, E. (2009). A Three-Layered Architecture for Real Time Path Planning and Obstacle Avoidance for Surveillance USVs Operating in Harbour Fields. Oceans 2009-Europe, 2009, Bremen, Germany, 18.
Chakravarthy, A. and Ghose, D. (1998). Obstacle avoidance in a dynamic environment: a collision cone approach. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 28, 562574.
Fossen, T. I., Breivik, M. and Skjetne, R. (2003). Line-of-sight path following of underactuated marine craft. IFAC Proceedings Volumes, 36, 211216.
Gammell, J. D., Srinivasa, S. S. and Barfoot, T. D. (2014). Informed RRT*: Optimal Sampling-Based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, USA, 29973004.
Garcia, C. E., Prett, D. M. and Morari, M. (1989). Model predictive control: theory and practice—a survey. Automatica, 25, 335348.
Gomez-Gil, J., Ruiz-Gonzalez, R., Alonso-Garcia, S. and Gomez-Gil, F. (2013). A Kalman filter implementation for precision improvement in low-cost GPS positioning of tractors. Sensors, 13, 1530715323.
Hart, P. E., Nilsson, N. J. and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4, 100107.
Jaillet, L., Yershova, A., La Valle, S. M. and Siméon, T. (2005). Adaptive Tuning of the Sampling Domain for Dynamic-Domain RRTs. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada, 2851–2856.
Jaillet, L., Cortés, J. and Siméon, T. (2010). Sampling-based path planning on configuration-space costmaps. IEEE Transactions on Robotics, 26, 635646.
Karaman, S. and Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30, 846894.
LaValle, S. M. and Kuffner, J. J. Jr. (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20, 378400.
Liu, Y. and Bucknall, R. (2016). The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method. Applied Ocean Research, 59, 327344.
Liu, Z.-Q., Wang, Y.-L. and Wang, T.-B. (2018). Incremental predictive control-based output consensus of networked unmanned surface vehicle formation systems. Information Sciences, 457, 166181.
Loe, Ø. A. G. (2008). Collision Avoidance for Unmanned Surface Vehicles. Institutt for teknisk kybernetikk, Trondheim, Norway.
Naeem, W., Irwin, G. W. and Yang, A. (2012). COLREGs-based collision avoidance strategies for unmanned surface vehicles. Mechatronics, 22, 669678.
Perera, L., Carvalho, J. and Soares, C. G. (2009). Autonomous Guidance and Navigation Based on the COLREGs Rules and Regulations of Collision Avoidance. Proceedings of the International Workshop: Advanced Ship Design for Pollution Prevention, Split, Croatia, 205–216.
Qin, Z., Lin, Z., Yang, D. and Li, P. (2017). A task-based hierarchical control strategy for autonomous motion of an unmanned surface vehicle swarm. Applied Ocean Research, 65, 251261.
Qiu, B., Wang, G., Fan, Y., Mu, D. and Sun, X. (2019). Adaptive sliding mode trajectory tracking control for unmanned surface vehicle with modeling uncertainties and input saturation. Applied Sciences, 9, 1240.
Qureshi, A. H., Mumtaz, S., Ayaz, Y., Hasan, O., Muhammad, M. S. and Mahmood, M. T. (2015). Triangular geometrized sampling heuristics for fast optimal motion planning. International Journal of Advanced Robotic Systems, 12, 10.
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G. 2010. Robotics: Modelling, Planning and Control. Springer Science & Business Media, Berlin, Germany.
Singh, Y., Sharma, S., Sutton, R. and Hatton, D. (2017). Path Planning of an Autonomous Surface Vehicle Based on Artificial Potential Fields in a Real Time Marine Environment. 16th International Conference on Computer and IT Applications in the Maritime Industries, Cardiff, UK.
Singh, Y., Sharma, S., Hatton, D. and Sutton, R. (2018a). Optimal path planning of unmanned surface vehicles. Indian Journal of Geo-Marine Sciences, 47(7), 13251334.
Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A. (2018b). A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Engineering, 169, 187201.
Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A. (2018c). Feasibility Study of a Constrained Dijkstra Approach for Optimal Path Planning of an Unmanned Surface Vehicle in a Dynamic Maritime Environment. 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, Portugal, 117122.
Statheros, T., Howells, G. and Maier, K. M. (2008). Autonomous ship collision avoidance navigation concepts, technologies and techniques. The Journal of Navigation, 61, 129142.
Tam, C. and Bucknall, R. (2013). Cooperative path planning algorithm for marine surface vessels. Ocean Engineering, 57, 2533.
Van den Berg, J., Abbeel, P. and Goldberg, K. (2011). LQG-MP: optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research, 30, 895913.
Wang, N., Su, S.-F., Pan, X., Yu, X. and Xie, G. (2018). Yaw-guided trajectory tracking control of an asymmetric underactuated surface vehicle. IEEE Transactions on Industrial Informatics, 15(6), 35023513.
Wang, N., Karimi, H. R., Li, H. and Su, S. (2019a). Accurate trajectory tracking of disturbed surface vehicles: a finite-time control approach. IEEE/ASME Transactions on Mechatronics, 24(3), 10641074.
Wang, N., Xie, G., Pan, X. and Su, S.-F. (2019b). Full-state regulation control of asymmetric underactuated surface vehicles. IEEE Transactions on Industrial Electronics, 66(11), 87418750.
Yang, C.-D., Yeh, F.-B. and Chen, J.-H. (1987). The closed-form solution of generalized proportional navigation. Journal of Guidance, Control, and Dynamics, 10, 216218.
Yershova, A., Jaillet, L., Siméon, T. and Lavalle, S. M. (2005). Dynamic-Domain RRTs: Efficient Exploration by Controlling the Sampling Domain. 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 3856–3861.
Zeng, Z., Sammut, K., Lammas, A., He, F. and Tang, Y. (2015). Efficient path re-planning for AUVs operating in spatiotemporal currents. Journal of Intelligent & Robotic Systems, 79, 135153.

Keywords

Online Heuristically Planning for Relative Optimal Paths Using a Stochastic Algorithm for USVs

  • Naifeng Wen (a1) (a2), Rubo Zhang (a1) (a2), Guanqun Liu (a1) (a2) and Junwei Wu (a1) (a2)

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