Method and system for predicting motion-outcome data of a robot moving between a given pair of robotic locations

Abstract

Systems and a method for predicting motion-outcome data of a robot moving between a given pair of robotic locations. Data on a given pair of robotic locations are received as input data. A function trained by a machine learning algorithm is applied to the input data, wherein a related robotic motion-outcome data is generated as output data. The robotic motion-outcome data is provided as output data.

1 - 15 . (canceled) 
     
     
         16 . A method for predicting, by a data processing system, motion-outcome data of a robot moving between a given pair of robotic locations, which comprises the following steps of:
 receiving data on the given pair of robotic locations as input data;   applying a function trained by a machine learning algorithm to the input data, wherein related robotic motion-outcome data is generated; and   providing the related robotic motion-outcome data as output data.   
     
     
         17 . The method according to  claim 16 , wherein the related robotic motion-outcome data is selected from the group consisting of:
 energy consumption data;   cycle time data;   robotic swept volume data;   joint movement data;   intermediate robotic locations data;   other types of motion-outcome data; and   any data set containing any combination of the above data.   
     
     
         18 . The method according to  claim 16 , which further comprises generating a conversion formatting table to map an original format of the input data received into a numerical format suitable for applying the function to the input data. 
     
     
         19 . The method according to  claim 16 , wherein the input data contains at least one information piece selected from the group consisting of:
 information on positions of the given pair of robotic locations;   information on robotic instructions at the given pair of robotic locations; and   information on a differential position between a robotic tool tip point and a robotic tool frame point.   
     
     
         20 . A data processing system, comprising:
 a processor; and   an accessible memory, the data processing system configured to:
 receive data on a given pair of robotic locations as input data; 
 apply a function trained by a machine learning algorithm to the input data, wherein related robotic motion-outcome data is generated; and 
 providing the related robotic motion-outcome data as output data. 
   
     
     
         21 . The data processing system according to  claim 20 , wherein the related robotic motion-outcome data is selected from the group consisting of:
 energy consumption data;   cycle time data;   robotic swept volume data;   joint movement data;   intermediate robotic locations data;   other types of motion-outcome data; and   any data set comprising any combination of the above data.   
     
     
         22 . The data processing system according to  claim 20 , wherein the data processing system is further configured to generate a conversion formatting table to map an original format of the input data received into a numerical format suitable for applying the function to the input data. 
     
     
         23 . The data processing system according to  claim 20 , wherein the input data contains at least one information piece selected from the group consisting of:
 information on positions of the given pair of robotic locations;   information on robotic instructions at the given pair of robotic locations;   information on a differential position between a robotic tool tip point and a robotic tool frame point.   
     
     
         24 . A non-transitory computer-readable medium encoded with executable instructions that, when executed, cause at least one data processing system to:
 receive data on a given pair of robotic locations as input data;   apply a function trained by a machine learning algorithm to the input data, wherein a related robotic motion-outcome data is generated;   providing the related robotic motion-outcome data as output data.   
     
     
         25 . The non-transitory computer-readable medium according to  claim 24 , wherein the related robotic motion-outcome data is selected from the group consisting of:
 energy consumption data;   cycle time data;   robotic swept volume data;   joint movement data;   intermediate robotic locations data;   other types of motion-outcome data; and   any data set comprising any combination of the above data.   
     
     
         26 . The non-transitory computer-readable medium according to  claim 24 , wherein said at least one data processing system is further configured to generate a conversion formatting table to map an original format of the input data received into a numerical format suitable for applying the function to the input data. 
     
     
         27 . The non-transitory computer-readable medium according to  claim 24 , wherein the input data contains at least one information piece selected from the group consisting of:
 information on positions of the given pair of robotic locations;   information on robotic instructions at the given pair of robotic locations; and   information on a differential position between a robotic tool tip point and a robotic tool frame point.   
     
     
         28 . A method for providing, by a data processing system, a function trained by a machine learning algorithm, which comprises the following steps of:
 receiving a plurality of robotic location pair data as input training data;   receiving a plurality of motion-outcome data as output training data, wherein the plurality of the motion-outcome data is related to the plurality robotic location pair data;   training by a machine learning algorithm a function based on the input training data and on the output training data; and   providing a trained function for predicting the motion-outcome data of a robot moving between a corresponding pair of robotic locations.   
     
     
         29 . A method for predicting, by a data processing system, motion-outcome data of a robot moving between a given pair of robotic locations, which comprises the following steps of:
 receiving a plurality of robotic location pair data as input training data;   receiving a plurality of motion-outcome data as output training data, wherein the plurality of motion-outcome data is related to the plurality robotic location pair data;   training by a machine learning algorithm a function based on the input training data and the output training data;   providing a trained function as a motion-outcome prediction module for predicting as the output data a motion-outcome data of a robot moving between a corresponding pair of robotic locations;   predicting the motion-outcome data by applying the motion-outcome prediction module to a given robotic location pair as the input data.   
     
     
         30 . A method for predicting, by a data processing system, motion-outcome data of a robot moving between a given pair of robotic locations, which comprises the following steps of:
 receiving a plurality of robotic location pair data as input training data;   receiving a plurality of motion-outcome data as output training data, wherein the plurality of motion-outcome data is related to the plurality of robotic location pair data;   training by a machine learning algorithm a function based on the input training data and the output training data;   providing a trained function as motion-outcome prediction module for predicting as output data motion-outcome data of a robot moving between a corresponding pair of robotic locations; and   predicting the motion-outcome data by applying the motion-outcome prediction module to a given robotic location pair as the input data.