An Intelligent Collision Avoidance Algorithm for Unmanned Surface Vehicles Based on Deep Reinforcement Learning
Bowen ZuXuan WangXuehua ZhouZhiguo Zhou
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.
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