English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper proposes a realistic modularized framework for controllingautonomous surface vehicles (ASVs) on inland waterways (IWs) based on deepreinforcement learning (DRL). The framework comprises two levels: a high-levellocal path planning (LPP) unit and a low-level path following (PF) unit, eachconsisting of a DRL agent. The LPP agent is responsible for planning a pathunder consideration of nearby vessels, traffic rules, and the geometry of thewaterway. We thereby transfer a recently proposed spatial-temporal recurrentneural network architecture to continuous action spaces. The LPP agent improvesoperational safety in comparison to a state-of-the-art artificial potentialfield method by increasing the minimum distance to other vessels by 65% onaverage. The PF agent performs low-level actuator control while accounting forshallow water influences and the environmental forces winds, waves, andcurrents. Compared with a proportional-integral-derivative (PID) controller,the PF agent yields only 61% of the mean cross-track error while significantlyreducing control effort in terms of the required absolute rudder angle. Lastly,both agents are jointly validated in simulation, employing the lower Elbe innorthern Germany as an example case and using real automatic identificationsystem (AIS) trajectories to model the behavior of other ships.

2-Level Reinforcement Learning for Ships on Inland Waterways