Dynamic Obstacle Avoidance for Service Robots Based on Spatio-Temporal Graph Attention Network
To solve the problems of collision,freezing,and the unnatural paths of service robots in dense crowds with autonomous decision-making ability,this study proposes a dynamic obstacle avoidance algorithm for service robots based on spatio-temporal graph attention network under the framework of Deep Reinforcement Learning(DRL).Spatio-temporal graph attention network represents the decision function of Proximal Policy Optimization(PPO)algorithm.First,the algorithm uses a Gated Recurrent Unit(GRU)to control the degree of memory and forgetting of the robot with respect to its environment and then extracts the time characteristics of that environment.This ensures the robot has a certain predictive effect on the movement trend of pedestrians.Second,the algorithm uses graph attention networks to obtain the spatially implicit interaction features between robots and pedestrians,enabling the robot to locate collision-free paths.Finally,the spatio-temporal graph attention network is trained under the PPO algorithm,which enables the robot to realize collision-free navigation tasks in a crowd.The algorithm is verified by simulation experiments in a dynamic closed environment of 2.5 m2 per capita.Compared with the non-learning Dynamic Window Algorithm(DWA),the navigation success rate of the proposed algorithm is improved by 71 percentage points.In addition,compared with the learning-type DSRNN-RL algorithm,the navigation success rate of the proposed algorithm is improved by 3 percentage points and the navigation path is shorter.Finally,a real-time navigation test in the Gazebo environment shows that the average inference time of the algorithm is 21.90 ms,which meets the requirements of real-time navigation.
service robotdynamic obstacle avoidanceDeep Reinforcement Learning(DRL)spatio-temporal graph attention networkreal-time navigation
国家科技期刊平台
NSTL
万方数据
维普
