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Method and module for distinguishing load of six-axis robot

The embodiment of the invention relates to the technical field of robot control, and discloses a method and a module for distinguishing a load of a six-axis robot. The method for distinguishing the load of the six-axis robot comprises the following steps: according to a preset parameter limiting value of each joint, generating an excitation track of the six-axis robot; when the six-axis robot carrying the load is controlled to run along the excitation track, collecting a parameter running value of each joint; according to the parameter running value of each joint and a preset robot dynamics model, calculating a torque predicting value of each joint; calculating a difference between the torque running value and the torque predicting value, the difference serving as torque produced by the load, and according to preset relationship of the torque produced by the load and a load inertia parameter, calculating the load inertia parameter. The embodiment of the invention also provides the module for distinguishing the load of the six-axis robot. The module for distinguishing the load of the six-axis robot disclosed by the embodiment has the advantage that compared with the prior art, the parameter running value of the robot is collected on line, and the load inertia parameter of the robot is distinguished; because a six-dimensional force sensor is not used, the cost is reduced.
Owner:SHANGHAI STEP ELECTRIC +1

Device waking up method and system for acoustic networking

The embodiment of the invention provides a device waking up method for an acoustic networking. The method comprises the steps that gain parameters of intelligent voice devices with various types are determined; the intelligent devices under the acoustic networking are monitored, and when the intelligent voice devices are woken by waking up commands, the voice frequency recorded by the intelligent voice devices and the confidence coefficient of the waking up commands are obtained; voice frequency energy recorded correspondingly are analyzed according to the gain parameters to achieve gain calibration, and according to the distance and the direction of all to-be-selected intelligent voice devices and a user, at least the distance and the direction of all the to-be-selected intelligent voice devices and the user, and the confidence coefficient of the waking up commands are input into a pre-trained neural network, and thus information fusion analyzing is conducted, and the intelligent devices for feeding back the user is woken. The embodiment of the invention further provides a device waking up system for the acoustic networking. According to the embodiment, the allocation requirements of intelligent voice in the acoustic networking are lowered, the target acoustic device for feeding back is effectively determined, and the interactive experience of the user is improved.
Owner:AISPEECH CO LTD
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