Abstract
A heuristic Dual sampling domain Reduction-based Optimal Rapidly-exploring Random Tree scheme is proposed by guiding the planning procedure of the optimal rapidly-exploring random tree (RRT*) method through learning environmental knowledge. The scheme aims to plan low fuel expenditure, easy-execution, and low collision probability paths online for an unmanned surface vehicle (USV) under constraints. First, an elliptic sampling domain, which is subject to an elliptic equation and the shortest obstacle avoidance path estimation, is created to plan short paths. Second, by the consideration of the USV motion states, obstacles and external interferences of the current, the near sampling domains of tree nodes are reduced to exclude high-cost sampling domains. Path feasibility is ensured by explicitly handling motion constraints. Third, a safe distance-based collision detection (CD) scheme and a velocity-based bounding box of USV are proposed to decrease the path collision probability. Additionally, a layered USV online path planning framework is built in accordance with the model predictive control method, and the path smoothing scheme is applied via the Dubins curve under the curvature constraint. Results demonstrate that the proposed dual sampling domain reduction method outperforms traditional reduction schemes in terms of improving the execution efficiency of RRT*. Meanwhile, the proposed CD method is more reliable than the conventional one.
Similar content being viewed by others
References
Campbell S, Naeem W, Irwin GW (2012) A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Ann Rev Control 36:267–283
Zheng Z, Sammut K, Lammas A, He F, Tang Y (2015) Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents. J Intell Rob Syst 79:135–153
Yershova A, Lavalle SM (2009) Motion planning for highly constrained spaces. Springer, London
Lazarowska A (2017) A new deterministic approach in a decision support system for ship’s trajectory planning. Expert Syst Appl 71:469–478
Du P, Liang H, Zhao S, Ahn CK (2019) Neural-based decentralized adaptive finite-time control for nonlinear large-scale systems with time-varying output constraints. IEEE Syst Man Cybern Syst 1:1
Zhang L, Lam H-K, Sun Y, Liang H (2019) Fault detection for fuzzy semi-Markov jump systems based on interval type-2 fuzzy approach. IEEE Trans Fuzzy Syst 1:1
Zhang L, Liang H, Sun Y, Ahn CK (2019) Adaptive event-triggered fault detection scheme for semi-Markovian jump systems with output quantization. IEEE Trans Syst Man Cybern: Syst 1:1
Liang H, Zhang H, Wang Z, Wang J (2014) Output regulation of state-coupled linear multi-agent systems with globally reachable topologies. Neurocomputing 123:337–343
Lavalle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Robot Res 20:348–400
Frazzoli E (2003) Quasi-random algorithms for real-time spacecraft motion planning and formation flight. Acta Astronaut 53:485–495
Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30:846–894
Noreen I, Khan A, Ryu H, Doh NL, Habib Z (2018) Optimal path planning in cluttered environment using RRT*-AB. Intel Serv Robot 11:41–52
Gammell JD, Srinivasa SS, Barfoot TD (2014) Informed RRT*: optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic. In: Presented at the 2014 IEEE/RSJ international conference on intelligent robots and systems, Chicago, USA
Islam F, Nasir J, Malik U, Ayaz Y, Hasan O, Khan M et al (2013) RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Rob Syst 10:299–305
Jaillet L, Cortés J, Siméon T (2010) Sampling-based path planning on configuration-space costmaps. IEEE Trans Rob 26:635–646
Liu Y, Bucknall R (2015) Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment. Ocean Eng 97:126–144
Zhao Y, Zheng Z, Zhang X, Liu Y (2017) Q learning algorithm based UAV path learning and obstacle avoidence approach. In: Presented at the 2017 36th Chinese Control Conference (CCC), Dalian, China, 2017
Challita U, Saad W, Bettstetter C (218) Deep Reinforcement Learning for Interference-Aware Path Planning of Cellular-Connected UAVs. In: Presented at the 2018 IEEE international conference on communications (ICC), Kansas City, USA
Hatzakis I, Wallace D (2006) Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Presented at the proceedings of the 8th annual conference on Genetic and evolutionary computation, Seattle, USA
Noreen I, Khan A, Habib Z (2016) A comparison of RRT, RRT* and RRT*-smart path planning algorithms. Int J Comput Sci Netw Secur (IJCSNS) 16:20–25
Liu ZQ, Wang YL, Wang TB (2018) Incremental predictive control-based output consensus of networked unmanned surface vehicle formation systems. Inf Sci 457:166–181
Tam CK, Richard B (2010) Collision risk assessment for ships. J Mar Sci Technol 15:257–270
Abbasi F, Mesbahi A, Mohammadpour VJ (2017) A new Voronoi-based blanket coverage control method for moving sensor network. IEEE Trans Control Syst Technol 27:409–417
Park BS, Yoo SJ (2019) An error transformation approach for connectivity-preserving and collision-avoiding formation tracking of networked uncertain underactuated surface vessels. IEEE Trans Cybern 49:2955–2966
Colito J (2007) Autonomous mission planning and execution for unmanned surface vehicles in compliance with the marine rules of the road. University of Washington, Master
Benjamin MR, Schmidt H, Newman PM, Leonard JJ (2013) Autonomy for unmanned marine vehicles with MOOS-IvP. Springer, New York
Loe ØAG (2008) Collision avoidance for unmanned surface vehicles. Norwegian University of Science and Technology, Master
Culligan KF (2006) Online trajectory planning for UAVs using mixed integer linear programming. Doctoral, Massachusetts Institute of Technology
Pongpunwattana A, Rysdyk R (2007) Evolution-based dynamic path planning for autonomous vehicles. Stud Comput Intell 70:113–145
Peng Z, Bo L, Chen X, Wu J (2012) Online route planning for UAV based on model predictive control and particle swarm optimization algorithm. In: Presented at the 10th world congress on intelligent control and automation (WCICA), Beijing, China, 2012
Nikolos IK, Valavanis KP, Tsourveloudis NC, Kostaras AN (2003) Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans Syst Man Cybern B Cybern 33:898–912
Peng X, Xu D (2012) Intelligent online path planning for UAVs in adversarial environments. Int J Adv Rob Syst 9:1–12
Wen NF, Su XH, Ma PJ, Zhao LL, Zhang YH (2015) Online UAV path planning in uncertain and hostile environments. Int J Mach Learn Cybernet 8:469–487
Wen NF, Su XH, Ma PJ, Zhao LL (2014) Sampling space reduction-based UAV online path planning algorithm in complex low altitude environments. ACTA Automatica Sinica 40:1376–1390
Gomez-Gil J, Ruiz-Gonzalez R, Alonso-Garcia S, Gomez-Gilm FJ (2013) A kalman filter implementation for precision improvement in low-cost GPS positioning of tractors. Sensors 13:15307–15323
Qiu B, Wang G, Fan Y, Mu D, Sun X (2019) Adaptive sliding mode trajectory tracking control for unmanned surface vehicle with modeling uncertainties and input saturation. Appl Sci 9:1240
Doa KD, Jiang ZP, Pana J (2004) Robust adaptive path following of underactuated ships. Automatica 40:929–944
Fossen T (1994) Guidance and control of ocean vehicles. Wiley, New York
Mousazadeh H, Kiapey A (2019) Experimental evaluation of a new developed algorithm for an autonomous surface vehicle and comparison with simulink results. China Ocean Eng 33:268–278
Muske KR, Ashrafiuon H, Haas G, Mccloskey R, Flynn T (2008) Identification of a control oriented nonlinear dynamic USV model. In: Presented at the American Control Conference, Seattle, USA, 2008
Beard RW, McLain TW, Nelson DB, Kingston D, Johanson D (2006) Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proc IEEE 94:1306–1324
Vandenberg J, Abbeel P, Goldberg K (2011) LQG-MP: optimized path planning for robots with motion uncertainty and imperfect state information. Int J Robot Res 30:895–913
Motus L (1993) Time concepts in real-time software. Control Eng Pract 1:21–33
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61673084) and Key Laboratory of Intelligent Perception and Advanced Control of State Ethnic Affairs Commission (Grant No. MD-IPAC-2019103).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wen, N., Zhang, R., Wu, J. et al. Online planning for relative optimal and safe paths for USVs using a dual sampling domain reduction-based RRT* method. Int. J. Mach. Learn. & Cyber. 11, 2665–2687 (2020). https://doi.org/10.1007/s13042-020-01144-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-020-01144-0