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

Autonomous systems are becoming ubiquitous and gaining momentum within themarine sector. Since the electrification of transport is happeningsimultaneously, autonomous marine vessels can reduce environmental impact,lower costs, and increase efficiency. Although close monitoring is stillrequired to ensure safety, the ultimate goal is full autonomy. One majormilestone is to develop a control system that is versatile enough to handle anyweather and encounter that is also robust and reliable. Additionally, thecontrol system must adhere to the International Regulations for PreventingCollisions at Sea (COLREGs) for successful interaction with human sailors.Since the COLREGs were written for the human mind to interpret, they arewritten in ambiguous prose and therefore not machine-readable or verifiable.Due to these challenges and the wide variety of situations to be tackled,classical model-based approaches prove complicated to implement andcomputationally heavy. Within machine learning (ML), deep reinforcementlearning (DRL) has shown great potential for a wide range of applications. Themodel-free and self-learning properties of DRL make it a promising candidatefor autonomous vessels. In this work, a subset of the COLREGs is incorporatedinto a DRL-based path following and obstacle avoidance system using collisionrisk theory. The resulting autonomous agent dynamically interpolates betweenpath following and COLREG-compliant collision avoidance in the trainingscenario, isolated encounter situations, and AIS-based simulations ofreal-world scenarios.

Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning