Design And Implementation On Vision Based Intelligent Grasping System Of Robot

For robots,grasping objects is an important ability.After several decades,robotic grasping has made great achievements and been widely used in industry,exploration,service,military and other fields.However,because of limitations,robots are not as flexible as humans to grasp objects.Affected by various factors of the objects,such as forms,materials and weights,and the complicated and changeable environment,the robotic grasping tasks still faces severe challenges.In this paper,the man-machine collaborative robots are used as the targets on intelligent grasping studies and controlled to grasp classified objects in the specific working environment.The visual information is regarded as a guidance for the robots.And through the deep learning based studies of the image feature representation,the robots can estimate the categories and positions of the objects,and can detect the suitable grasping positions on these objects.The issues involved in the proposed robotic intelligent grasping study include the object classification and location,the grasping position detection and pose estimation:1.In the aspect of the object classification and location,the object detection method based on deep learning is adopted.We create the data set of it and use the data set to train the deep neural network.After getting the trained model,we test its performance and analyze the test results.2.In the aspect of the grasping position detection,the grasping position detection method based on deep learning is proposed.Similarly,we also create the data set of the grasping position detection,and use it to train another neural network and get a trained model.We compare the test results with other results generated by other methods,and analyze their effectiveness.3.In the aspect of the pose estimation of the grasping position,we use the coordinate transformation method to convert the two-dimensional coordinates in the images to three-dimensional coordinates under the robots,and estimate their axes' orientations.Finally,we combine the deep learning based object detection,the deep learning based grasping position detection and the pose estimation of the grasping position to a visual information based intelligent grasping system of the robot in this paper.And we apply it to a real robot to do some experiments.The experimental results show that,the proposed grasping system can classify,locate and grasp objects rapidly.The proposed system can be applied to the grasping-related robotic tasks,and can be regarded as a reference for subsequent robotic researches.