Dynamic Path Planning Algorithm Based on an Optimization Model

  • Jingjing ZhangEmail author
  • Hongning Hu
  • Yuting Wan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)


Unmanned surface vessels (USVs) have been extensively employed in the past few decades. Traditional path planning algorithm assumes that obstacles remain stationary, and the USV dynamic constraints is not taken into account. In this paper, a path planning algorithm that considers time dimension and the dynamic performance of a USV is proposed. The algorithm abstracts the kinematics constraints and obstacle distance constraints into nonlinear constraints and abstracts the path planning problem into a nonlinear optimization model. The nonlinear model is approximated to a least squares model to improve the speed of solution. The experimental results show that this algorithm is reasonable and advantageous.


Path planning USV Nonlinear optimization Least squares Graph optimization 


  1. 1.
    Motwani, A.: A survey of uninhabited surface vehicles. Marine and Industrial Dynamic Analysis, School of Marine Science and Engineering, Plymouth University (2012)Google Scholar
  2. 2.
    Mooney, D.: Metric path planning. robotics notes (2009).
  3. 3.
    Yoo, S.-J., Park, J.-H., Kim, S.-H., Shrestha, A.: Flying path optimization in UAV-assisted IoT sensor networks. ICT Exp. 2(3), 140–144 (2016)CrossRefGoogle Scholar
  4. 4.
    Wang, Z., Zlatanova, S., Oosterom, P.V.: Path planning for first responders in the presence of moving obstacles with uncertain boundaries. IEEE Trans. Intell. Transp. 99, 1–11 (2017)Google Scholar
  5. 5.
    Zuo, L., Guo, Q., Xu, X., Fu, H.: A hierarchical path planning approach based on a* and least-squares policy iteration for mobile USVs. Neurocomputing 170(C), 257–266 (2015)CrossRefGoogle Scholar
  6. 6.
    Shum, A., Morris, K., Khajepour, A.: Direction-dependent optimal path planning for autonomous vehicles. USV. Autonom. Syst. 70, 202–214 (2015)CrossRefGoogle Scholar
  7. 7.
    Simmons, R., Henriksen, L., Chrisman, L.: Obstacle avoidance and safeguarding for a lunar rover. Proc AIAA Forum Adv. Develop. Space Robot. (1996)Google Scholar
  8. 8.
    Rösmann, C., et al.: Integrated online trajectory planning and optimization in distinctive topologies. USVics and Autonomous Systems (2016)Google Scholar
  9. 9.
    Rösmann, C., et al.: Timed-elastic-bands for time optimal point-to-point nonlinear model predictive control. European Control Conference (ECC), pp. 3352–3357 (2015)Google Scholar
  10. 10.
    Wu, B., Wen, Y., Huang, Y., Zhu, M.: Research of Unmanned Surface Vessel (USV) path planning algorithm based on ArcGIS. In: Proceedings of ICTIS 2013, pp. 2125–2134. ASCE (2013)Google Scholar
  11. 11.
    Phung, M.D., Cong, H.Q., Dinh, T.H., Ha, Q.: Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. Autom. Constr. 81, 25–33 (2017)CrossRefGoogle Scholar
  12. 12.
    Cui, R., Li, Y., Yan, W.: Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE TSMC. 46(7), 993–1004 (2016)Google Scholar
  13. 13.
    Devaurs, D., Simon, T., Corts, J.: Optimal path planning in complex cost spaces with sampling-based algorithms. IEEE Trans. Autom. Sci. Eng. 13(2), 415–424 (2016)CrossRefGoogle Scholar
  14. 14.
    Madsen, K., Nielsen, H.B., Tingleff, O.: Optimization with Constraints. 2nd Edn. (2004) Google Scholar
  15. 15.
    Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for Non-Linear Least Squares Problems. 2nd Edn. (2004)Google Scholar
  16. 16.
    Kummerle, R., Grisetti, G,, et al.: G2o: a general framework for graph optimization. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Warship Command and Fire Control Teaching and Research SectionCollege of Ordnance Engineering, Naval University of EngineeringWuhanChina
  2. 2.Unit 91053BeijingChina

Personalised recommendations