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Method and platform for predicating teleoperation of robot

A prediction method and robot technology, applied in the field of robots, can solve the problems of not fully reflecting the operation of the robot, no compensation for the real-time operation information of the robot, and no on-site sensor signals.

Active Publication Date: 2009-05-20
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this type of system has the following disadvantages: it can only predict commands, there is no on-site sensor signal, and there is no compensation for real-time operation information of the robot.
[0006] The feedback information only includes position information, but for robots, especially multi-degree-of-freedom humanoid robots, the compensation of only position information cannot fully reflect the operation of the robot

Method used

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  • Method and platform for predicating teleoperation of robot
  • Method and platform for predicating teleoperation of robot
  • Method and platform for predicating teleoperation of robot

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] see figure 1 , an embodiment of the present invention provides a robot teleoperation prediction method, including:

[0023] Step 101: receiving feedback data from the robot;

[0024] Step 102: Use the feedback data to calibrate the predictive model of the robot.

[0025] Concretely include in step 101:

[0026] Receive the joint angle data and pose data of the robot;

[0027] The joint angle data is specifically the angle of each joint angle of the robot in each degree of freedom direction, which is obtained by real-time measurement when the robot is running; the robot runs for one cycle, and each joint angle forms a joint angle angle-time in each direction of each degree of freedom. curve;

[0028] The pose data is specifically the position data and attitude data of the robot, which is obtained by real-time detection when the robot is running; the position data is the projection of the center of gravity of the robot on each coordinate axis of the three-dimensional ...

Embodiment 2

[0046] In this embodiment, taking the walking of a humanoid robot as an example, the robot teleoperation prediction method is described in detail:

[0047] The walking process of the robot is divided into three gait states, namely:

[0048] Step state 1: feet side by side upright;

[0049] Step state 2: left foot in front and right foot behind;

[0050] Step state 3: Right foot in front and left foot behind.

[0051] The above 3 gait states can be transformed into 6 gait patterns:

[0052] Gait mode 1: the left foot starts to walk as state 1 -> state 2;

[0053] Gait mode 2: the right foot starts to walk as state 1 -> state 3;

[0054] Gait mode 3: step forward with the left foot to state 3 -> state 2;

[0055] Gait mode 4: Stepping forward with the right foot is state 2 -> state 3;

[0056] Gait mode 5: the left foot retracts to state 3 -> state 1;

[0057] Gait pattern 6: The right foot retracts to state 2 -> state 1.

[0058] The above six gait patterns can be combi...

Embodiment 3

[0091] see image 3 , the embodiment of the present invention provides a teleoperation prediction platform, including:

[0092] Receiving module 301, for receiving the feedback data of robot;

[0093] The calibration module 302 is configured to calibrate the predictive model of the robot by using the feedback data.

[0094] The receiving module 301 is specifically used for:

[0095] Receive the joint angle data and pose data of the robot;

[0096] The joint angle data is specifically the angle of each joint angle of the robot in each degree of freedom direction, which is obtained by real-time measurement when the robot is running; the robot runs for one cycle, and each joint angle forms a joint angle angle-time in each direction of each degree of freedom. curve;

[0097] The pose data is specifically the position data and attitude data of the robot, which is obtained by real-time detection when the robot is running; the position data is the projection of the center of grav...

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Abstract

The embodiment of the invention provides a robot tele-operation prediction method and a robot tele-operation prediction platform, which belongs to the robot field. The method comprises the following steps: feedback data of a robot are received; and the feedback data are utilized to calibrate a prediction model of the robot. The tele-operation prediction platform comprises a receiving module and a calibration module. The embodiment of the invention obtains a predicted movement image of the robot by synchronously feedbacking angular data of joints and pose data of the robot to the tele-operation prediction platform and taking a mean value of the angular data of the joints and the pose data respectively which are obtained by repeated actual measurements, comprehensively reflects the operation condition of the robot, and finishes the tele-operation prediction.

Description

technical field [0001] The invention relates to the field of robots, in particular to a robot teleoperation prediction method and a teleoperation prediction platform. Background technique [0002] Teleoperation is an important technology for robot applications. Through the teleoperation platform, operators can monitor and control remote robots to complete various tasks, so that robots can replace humans in some unreachable, even some endangering human health or life safety. environment to complete various tasks. The existence of network delay has brought many problems to the perception and control of the remote operating system. Network delay can lead to system instability, which seriously reduces the operating performance of the system. [0003] Prediction is a key technology in teleoperation system. Prediction can overcome the influence of time delay in the teleoperation system, provide good guidance and instructions to the operator, and improve the operability of the sy...

Claims

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Application Information

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IPC IPC(8): B25J3/04B25J5/00B25J9/16B25J13/02G05B17/02G05D3/00
Inventor 黄强卢月品徐乾李敏李科杰
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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