Automatic Obstacle Avoidance Method of Mining Rescue Robot Based on Deep Learning
There are shortcomings in the automatic obstacle avoidance performance of rescue robots.In practice,the success rate of rescue and the obstacle avoidance smoothness coefficient are low,which cannot achieve the expected obstacle avoidance effect.Therefore,a deep learning based automatic obstacle avoidance method for mining rescue robots is proposed.Firstly,use an electronic compass and ultrasonic sensors to sense the azimuth and distance between the rescue robot and obstacles,and construct the spatial state of the rescue robot.Then,a deep learning network model with three convolutional layers and two fully connected layers is established,and a set of obstacle avoidance actions for rescue robots is constructed for training the deep learning network model.Finally,the spatial state information of the rescue robot is trained through the deep learning network model,and the spatial features of the rescue robot's movement are extracted to achieve automatic generation of obstacle avoidance decisions.Through experiments,it has been proven that after using the design method,the success rate of obstacle avoidance for rescue robots is above 95％,and the smoothness coefficient is above 0.85,which has good application prospects.
deep learningrescue robotautomatic obstacle avoidanceelectronic compassultrasonic sensorspatial state
NETL
NSTL
万方数据
