In order to improve the autonomous navigation performance of mobile robots in unknown environment,a target-driven autonomous navigation method is proposed.Without relying on prior map information,the mobile robot directly constructs candidate target points from the LiDAR readings,and selects the best points from the candidate target points as the local target points according to the robot's own position and the goal position.The improved local planner based on deep reinforcement learning directly obtains action signals from input information to achieve end-to-end control,enabling the mobile robot to reach local target points quickly and safely until it reaches the goal.Experiments show that the proposed navigation method can accomplish navigation tasks reliably and efficiently under complex environments.Compared with the nearest frontier navigation method,the average path length is reduced by 6.63%,the average running time is shortened by 19.11%,and the method has the advantages of high success rate,short path and fast speed.
NETL
NSTL
万方数据
