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

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 framew...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 71; H. 3; S. 2358 - 2374
Hauptverfasser: Wang, Ning, Zhang, Yuhang, Ahn, Choon Ki, Xu, Qingyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autonomous pilot
Autonomy
Constraints
Decision making
deep reinforcement learning
elite-duplication genetic algorithm
Genetic algorithms
Heuristic algorithms
Interpolation
Machine learning
Optimization
Path planning
path planning-tracking integration
Path tracking
Pilotage
Smoothing
Smoothing methods
Splines (mathematics)
Surface vehicles
Tracking errors
unmanned surface vehicle
Unmanned vehicles
Vehicle dynamics
Waypoints
ISSN:0018-9545, 1939-9359
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3136670