Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstra's searching, coastline expanding and genetic algorithm (GA). The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also implemented to provide the baseline for comparison. The proposed algorithm generates the most energy efficient paths in ten USV missions, while keeping a configurable clearance from the coastlines. The practicability and scalability of this algorithm is also demonstrated by analysing the computational time in these ten missions.

• Record URL:
  https://doi.org/10.1016/j.oceaneng.2019.106766
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0029801819308704
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Niu, Hanlin
  • Ji, Ze
  • Savvaris, Al
  • Tsourdos, Antonios
• Publication Date: 2020-1-15

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 106766
• Serial:
  • Ocean Engineering
  • Volume: 196
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Energy consumption; Genetic algorithms; Ocean currents; Ships; Trajectory control
• Candidate Terms: Unmanned surface vehicles
• Subject Areas: Energy; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 01736604
• Record Type: Publication
• Files: TRIS
• Created Date: Apr 22 2020 12:24PM