Research on Dynamic Obstacle Avoidance Method of Picking Robot Based on Deep Convolution CNN
In addition to static obstacles,there are also many dynamic obstacles in the plantation environ-ment,which interfere with the action of the picking robot.In order to improve the dynamic obstacle avoidance a-bility of picking robot,a new obstacle avoidance method is proposed by introducing deep convolution CNN.Re-al-time perception of external environment with stereo vision system to obtain depth image;use depth convolution CNN to extract depth image features and identify dynamic obstacles;according to the relative speed between the robot and the obstacle,the robot is properly adjusted to keep a safe distance,and the robot is controlled to move out of the collision area to realize the dynamic obstacle avoidance.The experimental results show that there is no collision between the picking robot and the obstacle under the research method,and the actual distance between the picking robot and the dynamic obstacle is greater than the 1m safety distance limit at each encounter.This proves that the research method is effective.
deep convolution CNNpicking robotstereo vision systemdynamic obstacle avoidance meth-od
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