Research on Robot Obstacle Avoidance Algorithm Based on Machine Vision
In order to solve the problem of limited obstacle avoidance ability in robot autonomous navigation,the YOLOv5s object detection model is adopted to achieve visual perception of obstacles.Subsequently,the two-dimensional coordinates of obstacles obtained by the visual perception algorithm are combined with the depth values of the depth camera to form real-time navigation information.In terms of obstacle avoidance,an adapted modified guidance vector field algorithm is proposed to achieve autonomous navigation function.Through validation on an AirSim-based simulation platform,the results show that the success rate of obstacle avoidance can reach 94%,indicating that the algorithm can enhance the robot's perception ability and improve the collision avoidance performance.
machine visionobstacle avoidanceAirSimYOLOv5sadapted modified guidance vector field
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