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

Authors
Wang, NingZhang, YuhangAhn, Choon KiXu, Qingyang
Issue Date
3월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Path planning; Heuristic algorithms; Vehicle dynamics; Genetic algorithms; Splines (mathematics); Smoothing methods; Decision making; Autonomous pilot; deep reinforcement learning; elite-duplication genetic algorithm; path planning-tracking integration; unmanned surface vehicle
Citation
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.71, no.3, pp.2358 - 2374
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume
71
Number
3
Start Page
2358
End Page
2374
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/140282
DOI
10.1109/TVT.2021.3136670
ISSN
0018-9545
Abstract
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.
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