计算机工程与应用2024，Vol.60Issue(19)：158-166,9.DOI:10.3778/j.issn.1002-8331.2307-0009
自注意力机制结合DDPG的机器人路径规划研究
Robot Path Planning Based on Self-Attention Mechanism Combined with DDPG
摘要
Abstract
In order to better solve the problems of low sample utilization,sparse reward and slow stability of network model in path planning of depth deterministic strategy gradient algorithm,an improved DDPG algorithm is proposed.By incorporating a self-attention mechanism into the image information obtained from robot camera sensors and using the Dot-product method to calculate the correlation between images,high weights can be accurately focused on obstacle infor-mation.In complex environments,it is difficult for robots to obtain positive feedback rewards due to their lack of experi-ence,which affects their exploration ability.Combining DDPG algorithm with HER,a DDPG-HER algorithm is pro-posed,which effectively utilizes positive and negative feedback to enable robots to learn appropriate rewards from both successful and failed experiences.A static and dynamic simulation environment is built by Gazebo for training and test-ing.The experimental results show that the proposed algorithm can significantly improve the sample utilization rate,accel-erate network model stability,and solve the problem of sparse reward,so that the robot can efficiently avoid obstacles and reach the target point in the path planning with unknown environment.关键词
深度强化学习/深度确定性策略梯度算法(DDPG)/后见经验算法(HER)/自注意力机制/机器人路径规划Key words
deep reinforcement learning/deep deterministic policy gradient(DDPG)/hindsight experience replay(HER)/self-attention mechanism,robot path planning分类
信息技术与安全科学引用本文复制引用
王凤英,陈莹,袁帅,杜利明..自注意力机制结合DDPG的机器人路径规划研究[J].计算机工程与应用,2024,60(19):158-166,9.基金项目
辽宁省应用基础研究计划(2023JH2/101300212) （2023JH2/101300212）
宿迁学院人才引进科研启动基金(校2022XRC091). （校2022XRC091）
