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Improved Soft Actor-Critic for Unmanned Underwater Vehicle Obstacle Avoidance
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DOI：10.1109/tits.2026.3698635.png)
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Abstract
En 中文
Traditional collision avoidance methods for unmanned underwater vehicles (UUVs) are highly dependent on the measurement accuracy of sensors and environmental maps and perform poorly in unknown dynamic environments. To improve the generalization ability of the UUV collision avoidance method when there is no prior knowledge of the underwater environment and sonar observation is uncertain, an improved soft actor-critic (SAC) method is proposed in this work. The improved SAC network architecture includes an encoder layer, a dynamic feature layer, and a merge layer. The long short-term memory (LSTM) network is integrated into the dynamic feature layer to capture historical state spaces and action spaces, and improve the collision policy’s memory capability. In this method, the state space is composed of forward-looking sonar perception, UUV motion state, and relative target position. In addition, to enhance the training efficiency and stability of the improved SAC, we design the continuous state space, the action space, and a composite reward function. We built a simulation platform and demonstrated that the improved SAC could effectively complete autonomous collision avoidance in environments with static and dynamic obstacles under external disturbances. The improved method enables the UUV to reach the target faster and reduces the path length and decision-making time of collision avoidance.
Keywords:
Unmanned underwater vehicle
improved soft actor-critic
collision avoidance
forward-looking sonar
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