Communication-Aware Motion Planning of AUV in Obstacle-Dense Environment: A Binocular Vision-Based Deep Learning Method
Jing Yan, Liang Zhang, Xian Yang, Cailian Chen, Xinping Guan
Abstract
Communication-aware motion planning of autonomous underwater vehicle (AUV) is regarded as an emergent requirement for marine intelligent transportation system. However, the fading acoustic channel and the complex underwater environment make it difficult to realize such task. This paper is concerned with a communication-aware motion planning issue for AUV in obstacle-dense environment. We first develop an intelligent AUV system, which includes binocular cameras for short-distance obstacle avoidance, sonars for long-distance detection, and modems for acoustic communication with buoys. For such system, the parallax angles from AUV to obstacles are utilized to construct an optimal motion planning problem by integrating our previously proposed channel estimation approach. In order to solve the above problem, a deep learning method called depth deterministic policy gradient (DDPG) is developed to minimize the cost function, such that a collision-free path can be planed for AUV while maintaining the communication quality. Note that the advantages of our solution are highlighted as: 1) balance the communication quality and motion stability over the disk model-based methods; 2) improve the collision-avoidance efficiency in path lengths and control efforts as compared with the distance-based methods. Finally, simulation and experimental studies are both provided to verify the effectiveness of our method.