Patent Publication Number: US-2020298400-A1

Title: Control system and control method of manipulator

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of PCT International Application No. PCT/EP2018/083461, filed on Dec. 4, 2018, which claims priority under 35 U.S.C. § 119 to Chinese Patent Application No. 201711285789.X, filed on Dec. 7, 2017. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to a control system and, more particularly, to a control system for a manipulator. 
     BACKGROUND 
     In order to improve the working precision of a manipulator, each arm of the manipulator generally has a very high stiffness, so that there will be no elastic deformation error in each arm of the manipulator. Thereby, special metal is often used to ensure the rigidity of the arm, which increases the weight and cost of the entire manipulator. In addition, in order to ensure the working precision of the manipulator, it is required that a transmission gear in each joint of the manipulator has very high precision, and a tooth gap between the transmission gears is very small. Moreover, other components of the manipulator should also have high precision, which also increases the cost. 
     The traditional rigid manipulator is usually controlled by a control system with fixed kinematics parameters. However, the control system with fixed structural parameters is not suitable for an elastic manipulator because the elastic manipulator has a large elastic deformation error and the structural parameters of the elastic manipulator will change continuously. 
     SUMMARY 
     A control system for a manipulator includes a position indicator provided on a flange for mounting a tool of the manipulator, a position detector provided near the manipulator and configured to detect a position information of the position indicator in real time, a computer calculating a position data of the position indicator in real time according to the position information, a cloud server calculating a working parameter of a joint of the manipulator in real time by an artificial intelligence neural network according to the position data, and a controller controlling the joint in real time based on the working parameter. The artificial intelligence neural network is a self-learning neural network that calculates and automatically adjusts a weight among a plurality of neurons based on the position data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The invention will now be described by way of example with reference to the accompanying Figures, of which: 
         FIG. 1  is a schematic diagram of a control system for a manipulator according to an embodiment; 
         FIG. 2  is a schematic diagram of a process of moving the manipulator by a manual teaching method; and 
         FIG. 3  is an illustrative simple schematic model of an artificial intelligence neural network. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENT(S) 
     Exemplary embodiments of the present disclosure will be described hereinafter in detail with reference to the attached drawings; wherein like reference numerals refer to like elements. The present disclosure may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein; rather; these embodiments are provided so that the present disclosure will convey the concept of the disclosure to those skilled in the art. 
     In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. In other instances, well-known structures and devices are schematically shown in order to simplify the drawing. 
     A control system for a manipulator according to an embodiment, as shown in  FIG. 1 , comprises at least one position indicator  210 , a position detector  220 , a controller  300 , a computer  400 , and a cloud server  500 . 
     As shown in  FIG. 1 , in an embodiment, the at least one position indicator  210  is provided on a flange  140  for mounting a tool  150  of the manipulator  100 . The position detector  220  is provided near the manipulator  100  and configured to detect position information of the position indicator  210  in real time. The computer  400  is adapted to calculate a position data of the position indicator  210  in real time according to the detected position information. The cloud server  500  is adapted to calculate working parameters of each joint  130  of the manipulator  100  in real time by an artificial intelligence neural network according to the calculated position data. The working parameters may comprise a rotation angle, a rotation speed, and an acceleration of a driving motor provided at each joint  130  of the manipulator  100 . The controller  300  is adapted to control each joint  130  in real time based on the calculated working parameters. 
       FIG. 3  shows an illustrative simple schematic model of an artificial intelligence neural network according to an exemplary embodiment of the present disclosure. As shown in  FIG. 3 , in an embodiment, the artificial intelligence neuron network is a self-learning neural network, which calculates and automatically adjusts a weight W between neurons N based on the input position data, so that the accommodation time, the steady-state error, and the trajectory error of the control system are minimal. 
     As shown in  FIG. 1 , in an embodiment, the position indicator  210  has an Ultra Wide Band (UWB) transmitter, the position detector  220  has an Ultra Wide Band receiver, and the position information includes a relative position of the Ultra Wide Band transmitter with respect to the Ultra Wide Band receiver obtained by the Ultra Wide Band receiver. The computer  400  is adapted to compute the position data of the position indicator  210  according to the relative position obtained by the Ultra Wide Band receiver. However, the present disclosure is not limited to this, for example, in another embodiment, the position indicator  210  may comprise a visual marker, the position detector  220  may comprise a camera, and the position information comprises an image of the visual marker captured by the camera. The computer  400  is adapted to process the image captured by the camera to obtain the position data of the position indicator  210 . 
     In order to increase the amount of position data, as shown in  FIG. 1 , in an embodiment, at least one position indicator  210  is provided on a base  110 , each arm  120 , and each joint  130  of the manipulator  100 . 
     In an embodiment, at least one arm  120  of the manipulator  100  is elastic, and the manipulator  100  has an elastic deformation error when subjected to a force. In an embodiment; the mechanical precision of the manipulator  100  is lower than a current industry design standard precision of a rigid manipulator. For example, the transmission gears of the manipulator  100  are allowed to have large tooth gaps, and the components of the manipulator  100  may have large dimensional errors. In this way, it may greatly decrease the cost of manufacturing the manipulator  100 . 
       FIG. 2  shows a process of moving the manipulator shown in  FIG. 1  by a manual teaching method according to an exemplary embodiment of the present disclosure. A method of controlling the manipulator  100  will be described with reference to  FIGS. 1-3  according to an exemplary embodiment of the present disclosure. The method may comprise steps of: 
     S 100 : as shown in  FIG. 1 , providing the control system according to any one embodiment as mentioned above; 
     S 200 : as shown in  FIGS. 1 and 2 , controlling a tool center point TCP of the manipulator  100  by a manual teaching method to move the tool center point TCP from a first point A to a second point B along a plurality of different paths LAB 1 , LAB 2 , respectively, and calculating the position data of the position indicator  210  at the first point A and the second point B; 
     S 300 : as shown in  FIGS. 2 and 3 , inputting the calculated position data into the artificial intelligence neuron network operated on the cloud server  500 , wherein the artificial intelligence neuron network calculates and automatically adjusts the weight W among neurons N based on the input position data so that the accommodation time, the steady-state error, and the trajectory error of the control system are minimal. The artificial intelligence neuron network improves the control accuracy of the control system. 
     As shown in  FIG. 3 , in an embodiment, only two paths LAB 1 , LAB 2  are shown, But, it is appreciated for those skilled in this art, times that the manipulator  100  is moved from the first point A to the second point B should reach a certain amount, so that the weights W among the neurons N of the artificial intelligence neural network may be adjusted to the optimum, so as to minimize the accommodation time, the steady-state error and the trajectory error of the control system. Thereby, the times that the manipulator  100  is moved from the first point A to the second point B along the paths LAB 1 , LAB 2 , respectively, is usually not less than 10 times. 
     As shown in  FIGS. 2-3 , in an embodiment, the above method may further comprise steps of: 
     S 400 : controlling the tool center point TCP of the manipulator  100  by the manual teaching method to move the tool center point from the second point B to a third point C along a plurality of different paths LAC 1 , LAC 2 , respectively, and calculating the position data of the position indicator  210  at the second point B and the third point C; 
     S 500 : inputting the calculated position data into the artificial intelligence neuron network operated on the cloud server  500 , wherein the artificial intelligence neuron network calculates and automatically adjusts the weight W among the neurons N based on the input position data so that the accommodation time, the steady-state error and the trajectory error of the control system are minimal. 
     As shown in  FIGS. 2-3 , in an embodiment, the above method may further comprise steps of: 
     S 600 : controlling the tool center point TCP of the manipulator  100  by the manual teaching method to move the tool center point from a current point to a next point along a plurality of different paths, respectively, and calculating the position data of the position indicator  210  at the current point and the next point; 
     S 700 : inputting the calculated position data into the artificial intelligence neuron network operated on the cloud server  500 , wherein the artificial intelligence neuron network calculates and automatically adjusts the weight W among the neurons N based on the input position data so that the accommodation time, the steady-state error and the trajectory error of the control system are minimal. 
     As shown in  FIGS. 2-3 , in an embodiment, there are a plurality of key points in a working area of the manipulator  100 , the key points at least comprises the first point A, the second point B, the third point C, the current point, and the next point. The above method may further comprise a step of: 
     S 800 : repeating the steps S 600  and S 700  until the manipulator  100  has been moved to all key points. 
     As shown in  FIG. 2 , in an embodiment, the posture of the tool  150  remains unchanged while the tool center point TCP of the manipulator  100  is moved from one point A to another point B along one path LAB 1  or LAB 2 . The posture of the tool  150  while the tool center point TCP of the manipulator  100  is moved from one point A to another point B along one path LAB 1  is different from the posture of the tool  150  while the tool center point TCP is moved from one point A to another point B along another path LAB 2  different from the one path LAB 1 . But the present disclosure is not limited to this; in another embodiment, the posture of the tool  150  may be changeable while the tool center point TCP is moved from one point A to another point  13  along one path LAB 1 , LAB 2 . 
     As shown in  FIG. 2 , in an embodiment, the tool  150  mounted on the manipulator  100  are in an unloaded state without gripping any work piece in the above steps S 100 -S 800 . 
     In another embodiment, in order to enable the artificial intelligence neural network of the manipulator control system to adapt to a load state better, after completing the steps S 100 -S 800 , the tool  150  mounted on the manipulator  100  is in a load state of gripping a work piece; and the above method may further comprise a step of: 
     S 900 : repeating the steps S 200  and S 300 . 
     It should be appreciated for those skilled in this art that the above embodiments are intended to be illustrated, and not restrictive. For example, many modifications may be made to the above embodiments by those skilled in this art, and various features described in different embodiments may be freely combined with each other without conflicting in configuration or principle. Although several exemplary embodiments have been shown and described, it would be appreciated by those skilled in the art that various changes or modifications may be made in these embodiments without departing from the principles and spirit of the disclosure; the scope of which is defined in the claims and their equivalents. 
     As used herein, an element recited in the singular and proceeded with the word “a” or “an” should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to “one embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments “comprising” or “having” an element or a plurality of elements having a particular property may include additional such elements not having that property.