【摘要】 传统算法在自主水下航行器(AUV)面临的高维空间和动态障碍物环境中存在计算负担大、精度不足问题。针对复杂三维环境下动态障碍物对AUV路径规划的挑战,文中提出一种基于改进的双重深度Q网络(DDQN)的AUV三维路径规划方法。通过网络结构优化和高效奖励函数设计,显著提升了AUV路径规划效率和精度。此外,引入了动态障碍物运动轨迹建模,采用辛格模型和卡尔曼滤波算法实现障碍物状态的精确预测,增强了AUV的动态避障能力。同时,采用Basis样条函数对路径进行平滑处理,改善了AUV航行路径的连续性与稳定性。仿真与实验结果表明,所提方法在复杂动态环境下能够有效避免碰撞,实时规划出安全且高效的航行路径。相比传统方法, DDQN算法在路径长度、避障成功率及计算效率方面均展现出明显优势,有效解决了动态障碍环境中AUV的三维路径规划问题。更多还原

【Abstract】 Traditional algorithms suffer from heavy computational burden and insufficient accuracy in the high-dimensional space and dynamic obstacle environment faced by autonomous underwater vehicles(AUVs). To overcome the challenges posed by dynamic obstacles to AUV path planning in complex three-dimensional environments, this study proposed a threedimensional path planning method for AUVs based on an enhanced double deep Q-network(DDQN). By optimizing the network architecture and designing an efficient reward function, the AUV path planning efficiency and accuracy were significantly improved. Moreover, dynamic obstacle trajectories were modeled, and the Singer model, combined with the Kalman filter algorithm, was used to precisely predict obstacle states, thereby enhancing the dynamic obstacle avoidance capabilities of AUVs. Additionally, Basis spline functions were utilized to smooth the paths, thereby improving the path continuity and stability of AUVs. Simulation and experimental results demonstrate that the proposed approach effectively avoids collisions in complex dynamic environments and achieves real-time planning of safe and efficient paths. Compared to traditional methods, the DDQN algorithm shows significant advantages in terms of path length, obstacle avoidance success rate, and computational efficiency, effectively addressing the challenges associated with three-dimensional path planning of AUVs in dynamic obstacle environments.更多还原