EconPapers    
Economics at your fingertips  
 

Collision-avoidance under COLREGS for unmanned surface vehicles via deep reinforcement learning

Yong Ma, Yujiao Zhao, Yulong Wang, Langxiong Gan and Yuanzhou Zheng

Maritime Policy & Management, 2020, vol. 47, issue 5, 665-686

Abstract: Collision avoidance for unmanned surface vehicles (USVs) is significant for the fulfillment of autonomous navigation. Generally, classical collision-avoidance algorithms are proposed for relatively simple encounter situation, in this scenario only two USVs are stressed. Furthermore, to generate the rational manoeuvre operations, it is necessary that USVs should abide by International Regulations for Preventing Collision at Sea (COLREGS). However, COLREGS has not paid attention to rules for multi-USV collision-avoidance problem. Furthermore, those collision-avoidance rules in COLREGS have not been quantified for USVs. Following that, this paper utilizes deep reinforcement learning (DRL) algorithm to resolve collision-avoidance for USVs even in complex encounter situations. Within our DRL algorithm, related COLREGS is quantified properly and integrated into the DRL model, and then encounter situation of USVs is formulated as environmental observation value, accordingly a set of decision making is reached by decision-making neural network, and the reward function is designed for updating network parameters iteratively. Consequently, collision avoidance for USVs can be achieved eventually. By employing our DRL algorithm, collision avoidance for USVs under generous complex scenarios are resolved with the aid of corresponding intelligent decision-making operations. Simulation results verify the effectiveness of our DRL algorithm.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03088839.2020.1756494 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:marpmg:v:47:y:2020:i:5:p:665-686

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TMPM20

DOI: 10.1080/03088839.2020.1756494

Access Statistics for this article

Maritime Policy & Management is currently edited by Dr Kevin Li and Heather Leggate McLaughlin

More articles in Maritime Policy & Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:marpmg:v:47:y:2020:i:5:p:665-686