Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-27T18:44:30.688Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  23 December 2019

Naifeng Wen ,
Rubo Zhang ,
Guanqun Liu  and
Junwei Wu
Naifeng Wen*
Affiliation:
(School of Electromechanical Engineering, Dalian Minzu University, Dalian, China) (Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian, China)
Rubo Zhang*
Affiliation:
(School of Electromechanical Engineering, Dalian Minzu University, Dalian, China) (Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian, China)
Guanqun Liu
Affiliation:
(School of Electromechanical Engineering, Dalian Minzu University, Dalian, China) (Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian, China)
Junwei Wu
Affiliation:
(School of Electromechanical Engineering, Dalian Minzu University, Dalian, China) (Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission, Dalian, China)
*
(E-mail: [email protected]; [email protected])
(E-mail: [email protected]; [email protected])
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Path OptimisationUnmanned Surface VehiclesOnline Path PlanningSampling Space ReductionProportional Navigation Guidance
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 2 , March 2020 , pp. 485 - 508
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

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, 412–417.CrossRefGoogle Scholar
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, 1306–1324.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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, 669–688.CrossRefGoogle Scholar
Bibuli, M., Caharija, W., Pettersen, K. Y., Bruzzone, G., Caccia, M. and Zereik, E. (2014). ILOS guidance – experiments and tuning. IFAC Proceedings Volumes, 47, 4209–4214.CrossRefGoogle Scholar
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, 378–383.CrossRefGoogle Scholar
Breivik, M., Hovstein, V. E. and Fossen, T. I. (2008). Straight-line target tracking for unmanned surface vehicles. Modeling, Identification and Control, 29(4), 131–149.CrossRefGoogle Scholar
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, 62–71.CrossRefGoogle Scholar
Caccia, M., Bibuli, M., Bono, R. and Bruzzone, G. (2008). Basic navigation, guidance and control of an unmanned surface vehicle. Autonomous Robots, 25, 349–365.CrossRefGoogle Scholar
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, 386–391.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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, 1–8.CrossRefGoogle Scholar
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, 562–574.CrossRefGoogle Scholar
Fossen, T. I., Breivik, M. and Skjetne, R. (2003). Line-of-sight path following of underactuated marine craft. IFAC Proceedings Volumes, 36, 211–216.CrossRefGoogle Scholar
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, 2997–3004.CrossRefGoogle Scholar
Garcia, C. E., Prett, D. M. and Morari, M. (1989). Model predictive control: theory and practice—a survey. Automatica, 25, 335–348.CrossRefGoogle Scholar
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, 15307–15323.CrossRefGoogle ScholarPubMed
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, 100–107.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Jaillet, L., Cortés, J. and Siméon, T. (2010). Sampling-based path planning on configuration-space costmaps. IEEE Transactions on Robotics, 26, 635–646.CrossRefGoogle Scholar
Karaman, S. and Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30, 846–894.CrossRefGoogle Scholar
LaValle, S. M. and Kuffner, J. J. Jr. (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20, 378–400.CrossRefGoogle Scholar
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, 327–344.CrossRefGoogle Scholar
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, 166–181.CrossRefGoogle Scholar
Loe, Ø. A. G. (2008). Collision Avoidance for Unmanned Surface Vehicles. Institutt for teknisk kybernetikk, Trondheim, Norway.Google Scholar
Naeem, W., Irwin, G. W. and Yang, A. (2012). COLREGs-based collision avoidance strategies for unmanned surface vehicles. Mechatronics, 22, 669–678.CrossRefGoogle Scholar
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.Google Scholar
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, 251–261.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G. 2010. Robotics: Modelling, Planning and Control. Springer Science & Business Media, Berlin, Germany.Google Scholar
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.Google Scholar
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), 1325–1334.Google Scholar
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, 187–201.CrossRefGoogle Scholar
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, 117–122.Google Scholar
Statheros, T., Howells, G. and Maier, K. M. (2008). Autonomous ship collision avoidance navigation concepts, technologies and techniques. The Journal of Navigation, 61, 129–142.CrossRefGoogle Scholar
Tam, C. and Bucknall, R. (2013). Cooperative path planning algorithm for marine surface vessels. Ocean Engineering, 57, 25–33.CrossRefGoogle Scholar
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, 895–913.CrossRefGoogle Scholar
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), 3502–3513.CrossRefGoogle Scholar
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), 1064–1074.CrossRefGoogle Scholar
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), 8741–8750.CrossRefGoogle Scholar
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, 216–218.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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, 135–153.CrossRefGoogle Scholar