Autonomous Pilot of Unmanned Surface Vehicles: Bridging Path Planning and Tracking

Ning Wang, Yuhang Zhang, Choon Ki Ahn, Qingyang Xu

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)


Autonomous pilot is crucial in integrally promoting the autonomy of an unmanned surface vehicle (USV). However, the integration mechanism of decision and control is still unclear within the entire autonomy. In this paper, by organically bridging path planning and tracking, an autonomous pilot framework with waypoints generation, path smoothing and policy guidance of a USV in congested waters is established, for the first time. Incorporating elite and diversity operations into the genetic algorithm (GA), an elite-duplication GA (EGA) strategy is devised to optimally generate sparse waypoints in a constrained space. The B-spline technique is further deployed to make flexibly smooth interpolation facilitating path smoothing supported by optimal sparse-waypoints. Seamlessly bridged by the parametric smooth path, deep reinforcement learning (DRL) technique is resorted to continuously extract in-depth pilotage policies, i.e., mappings from path tracking errors, collision risks and control constraints to continuous control forces/torques. Eventually, the entire spline-bridged EGA-DRL (SED) framework merits autonomous global-pilotage and local-reaction in an organically modular manner. Comprehensive validations and comparisons in various real-world geographies demonstrate the effectiveness and superiority of the proposed SED autonomous pilot framework.

Original languageEnglish
Pages (from-to)2358-2374
Number of pages17
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - 2022 Mar 1

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.


  • Autonomous pilot
  • deep reinforcement learning
  • elite-duplication genetic algorithm
  • path planning-tracking integration
  • unmanned surface vehicle

ASJC Scopus subject areas

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering


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