A method for grabbing objects with a robotic arm based on deep learning
A technology of deep learning and manipulators, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of insufficient grasping stability of different objects, knocking down of objects to be grasped, etc., to solve the problem of unbalanced grasped objects or Knock down the grasping object, stabilize the grasping, improve the effect of accuracy and stability
Active Publication Date: 2020-03-10
SHANDONG UNIV OF SCI & TECH
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[0003] The joint motor rotates at a certain angle, finds a reasonable path through path planning, turns to the target, and grasps by hand, but has the following disadvantages: the grasping stability of different objects is insufficient, and most robotic arms only grasp a single specific structure, and it is easy to knock down the object to be grasped
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[0060]Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:
[0061] A method for grabbing objects with a robotic arm based on deep learning, the process is as follows figure 1 As shown in the figure, using a binocular camera, a workstation, and a multi-degree-of-freedom robotic arm to realize the voice control of the robotic arm to grasp objects, ideally capture the object to be grasped, record the angles of the motors of each joint of the robotic arm at this time, and make a good mapping relationship, an object corresponds to a set of theoretical angle values of the manipulator motor; the specific steps include:
[0062] Step 1: Specific person speech training; specifically includes the following steps:
[0063] Step 1.1: Preprocess the speech signal sequence X(n) to obtain the sequence X m After (n), perform Fourier transform:
[0064] X(i,k)=FFT[X m (n)];
[0065] Ordinary line energy: E(i,k)=[X(...
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The invention discloses a method for grabbing objects by the hand of a manipulator based on deep learning, which belongs to the technical field of multi-degree-of-freedom manipulator control. Before grabbing things, the deep learning network architecture is used to prepare labels in advance and perform training and classification. One label corresponds to A grasping angle database is prepared. When the user speaks an instruction, the object to be grasped is determined through voice recognition, and then the object is found through image recognition and positioning, and the image coordinates and the angle of the mechanical arm's hand are returned. The group-optimized BP neural network corrects the image coordinates, and finally the GRNN network is used to inversely solve the angles that each motor needs to rotate, and the manipulator completes the grasp after turning to the target. The invention can realize grasping of a selected target, and simultaneously avoids the problem of unstable grasping.
Description
technical field [0001] The invention belongs to the technical field of multi-degree-of-freedom manipulator control, and in particular relates to a method for grasping an object by a manipulator hand based on deep learning. Background technique [0002] With the continuous development of society, people's demand for social services will also increase, and the elderly and disabled people have also become the focus of attention. The rapid growth of the elderly population has led to the seriousness of aging in our country. According to statistics, the population over the age of 60 has reached more than 230 million in 2016, but there are not so many nursing staff to take care of these elderly people. Not only that, a large number of disabled people also need a large number of nursing staff. Traditional nursing methods can no longer meet the needs of the current social situation, and advanced nursing robots will improve the lives of the elderly and the disabled. As nursing robo...
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IPC IPC(8): B25J9/16B25J9/22
CPCB25J9/0081B25J9/16B25J9/1679
Inventor 王传江侯鹏亮王栋朱坤怀张远来袁振孙秀娟
Owner SHANDONG UNIV OF SCI & TECH