Rendezvous Path Planning for Multiple Autonomous Marine Vehicles

Zheng Zeng, Karl Sammut, Lian Lian, Andrew Lammas, Fangpo He, Youhong Tang

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


In this paper, a distributed shell-space decomposition (DSSD) scheme is proposed for rendezvous trajectory planning of multiple autonomous marine vehicles (AMVs); this category of vehicle includes both autonomous underwater vehicles and autonomous surface vessels. The DSSD extends the concept of shell-space decomposition (SSD) by generating multiple sets of shells radiating out from the starting position of each vehicle to the rendezvous destination, enabling each vehicle to generate its trajectory within its own SSD subset. This scheme is combined with an optimized mass-center rendezvous-point selection scheme, together with a B-spline-based quantum particle swarm optimization technique to find optimal rendezvous trajectories for multiple AMVs with minimal travel time and simultaneous time of arrival for all the participating vehicles. The path planner identifies the optimal rendezvous location and generates the corresponding rendezvous trajectories based on the capabilities of each vehicle and the dynamics of the ocean environment. Simulation results show that the proposed DSSD method, combined with a novel optimized mass-center rendezvous-point selection scheme, is able to find trajectories for multiple AMVs that ensure that they reach their common destination simultaneously and with optimized time/energy consumption. A set of representative Monte Carlo simulations were run to analyze the performance of these path planners for multiple AMVs rendezvous. The results demonstrate the inherent robustness and superiority of the proposed planner based on the combined DSSD method and optimized mass-center rendezvous-point selection scheme, in comparison with other techniques.

Original languageEnglish
Pages (from-to)640-664
Number of pages25
Issue number3
Publication statusPublished - Jul 2018


  • Evolutionary algorithm
  • multiple autonomous marine vehicles (AMVs)
  • optimization
  • path planning
  • space decomposition


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