融合速度障碍法和DQN的无人船避障方法  

Obstacle avoidance method for USV combining velocity obstaclemethod and DQN

作　　者：宗律 李立刚[1,3] 贺则昊 韩志强 戴永寿 Zong Lyu;Li Ligang;He Zehao;Han Zhiqiang;Dai Yongshou(College of Ocean and Space Information,China University of Petroleum(East China),Qingdao 266580,China;College of Control Science and Engineering,China University of Petroleum(East China),Qingdao 266580,China;Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation,Ministry of Natural Resources,Qingdao 266580,China)

机构地区：[1]中国石油大学(华东)海洋与空间信息学院,青岛266580 [2]中国石油大学(华东)控制科学与工程学院,青岛266580 [3]自然资源部海上丝路海洋资源环境组网观测技术创新中心,青岛266580

出　　处：《电子测量技术》2024年第20期60-67,共8页Electronic Measurement Technology

基　　金：国家自然科学基金(42274159);中央高校基本科研业务费专项资金(24CX02030A)项目资助。

摘　　要：为提高无人船(USV)动态避障的安全性与经济性,提出了一种融合速度障碍法和深度Q网络(DQN)的无人船避障方法。首先,在计算传统速度障碍物相对碰撞区域时,考虑障碍物未来时刻运动信息,改善传统速度障碍法因忽略障碍物即时位置变化从而导致避障失败的问题。其次,将碰撞危险度系数引入DQN状态空间中,优先选取危险度系数最高的障碍物作为避障对象,改善状态空间信息冗余问题。再次,根据改进速度障碍法避障思想重新设计奖励函数,确定无人船避障时机与转向角度,解决传统DQN的奖励稀疏问题,提高其学习效率与收敛速度。最后,为验证该方法性能,与3种主流避障方法进行了仿真实验,实验结果表明,该方法能够为无人船提供合适的避障方向,使无人船航行路径更为经济和安全。此外,通过实船实验验证了该方法具有一定的工程实用价值。To enhance the safety and efficiency of dynamic obstacle avoidance for unmanned surface vessels(USV),an obstacle avoidance method combining the velocity obstacle method and deep Q-network(DQN)is proposed.First,when calculating the traditional velocity obstacle′s relative collision region,the future movement information of obstacles is considered.This improvement addresses the issue of obstacle avoidance failure caused by ignoring the real-time position changes of obstacles in the traditional velocity obstacle method.Second,a collision risk coefficient is introduced into the DQN state space,prioritizing obstacles with the highest risk coefficient as avoidance targets,thereby reducing redundancy in state space information.Third,a reward function is redesigned based on the improved velocity obstacle method′s obstacle avoidance concept,determining the timing and steering angle for USV obstacle avoidance.This solves the sparse reward problem in traditional DQNs,enhancing their learning efficiency and convergence speed.Finally,to validate the performance of this method,simulation experiments were conducted comparing it with three mainstream obstacle avoidance methods.The results show that this method can provide suitable avoidance directions for USVs,making their navigation paths more economical and safer.Additionally,real-ship experiments confirmed the method′s practical engineering value.

关 键 词：无人船 避障 DQN 奖励函数 速度障碍法 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TN966[自动化与计算机技术—控制科学与工程]