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Research paper

An Intelligent Collision Avoidance Algorithm for Unmanned Surface Vehicles Based on Deep Reinforcement Learning

Bowen Zu , Xiang Wang , Xuehua Zhou , Zhiguo Zhou ( )
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China

This paper was recommended for publication in its revised form by editorial board member, Christos Verginis.

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Abstract

To address the obstacle avoidance challenge for unmanned surface vehicles, this paper presents a novel intelligent algorithm based on deep reinforcement learning. The algorithm incorporates human demonstration experience data for quick convergence and efficient decision-making. It features an end-to-end framework for multi-sensor data processing and immediate action decisions. Both simulation and deployment experiments evidence the superiority of this algorithm.

Keywords

Unmanned surface vehiclesobstacle avoidancedeep reinforcement learningartificial intelligence

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Unmanned Systems
Volume 14 Issue 2,
March 2026
Pages 505-522
DOI: 10.1142/S2301385026500135

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Cite this article:
Zu B, Wang X, Zhou X, et al. An Intelligent Collision Avoidance Algorithm for Unmanned Surface Vehicles Based on Deep Reinforcement Learning. Unmanned Systems, 2026, 14(2): 505-522. https://doi.org/10.1142/S2301385026500135

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Received: 09 October 2024
Accepted: 04 February 2025
Published: 24 June 2025
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