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A Repeated Motion Planning Method for Redundant Robot Using Parabolic Final State Neural Network

A neural network, repetitive motion technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve the problems of inability to converge in a limited time, difficult to achieve, and low calculation accuracy

Active Publication Date: 2021-01-01
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0025] In order to overcome the shortcomings of the existing redundant robot repetitive motion planning method that cannot converge in a limited time, the calculation accuracy is low, and it is not easy to implement, the present invention provides a parabolic final state that has a limited time convergence, high calculation accuracy, and is easy to implement. Redundant robot motion planning method based on neural network

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  • A Repeated Motion Planning Method for Redundant Robot Using Parabolic Final State Neural Network
  • A Repeated Motion Planning Method for Redundant Robot Using Parabolic Final State Neural Network
  • A Repeated Motion Planning Method for Redundant Robot Using Parabolic Final State Neural Network

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Embodiment Construction

[0099]The present invention will be further described below in conjunction with the drawings.

[0100]ReferenceFigure 1~Figure 9, A redundant robot repetitive motion planning method using parabolic final state neural network,figure 1 The flow chart of the redundant robot repetitive motion planning scheme consists of the following three steps: 1. Determine the expected trajectory and expected joint angle of the redundant robot end effector; 2. Use the asymptotic convergence performance index and form redundant robot repetition Motion secondary planning scheme; 3. Solve the secondary planning problem with a parabolic final state neural network to obtain the trajectory of each joint angle, including the following steps:

[0101]1) Determine the desired trajectory

[0102]Set the expected joint angle of the redundant robot PA10Determine the center coordinates of the circle track (x=0.2m, y=0, z=0), set the radius of the circle to 0.2m, and the angle between the circular surface and the xy plane ...

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Abstract

The invention discloses a redundant robot repetitive motion planning method adopting a parabolic final state neural network. The redundant robot repetitive motion planning method adopting the parabolic final state neural network comprises the following steps: giving an expected trajectory rd(t) of a robot end-actuator in cartesian space, and giving expected folding angles theta d (0) of each joint; and taking the parabolic final state neural network as a solver for repetitive motion of a robot by converting a quadratic optimizing problem about redundant robot trajectory planning into a time-varying matrix equation solving problem by adopting an asymptotically convergent performance index. In the situation of initial position offset, a repetitive motion planning task with finite time convergence of the redundant robot is realized. The invention provides the redundant robot repetitive motion planning method adopting the parabolic final state neural network, which has the finite time convergence, high computing accuracy and easiness in implementation.

Description

Technical field[0001]The invention relates to a repetitive motion planning technology for an industrial robot. Specifically, a redundant robot repetitive motion planning method using a parabolic final state neural network with limited time convergence and an initial position deviating from a desired trajectory is proposed.Background technique[0002]Redundant robots have good flexibility and fault tolerance. They can use extra degrees of freedom to enhance obstacle avoidance without affecting the operation of the end effector, and can complete variable tasks in a complex working environment. At present, redundant robots have played an important role in many application fields, such as manufacturing, medical equipment, logistics and transportation, and military defense.[0003]The number of joints n of the redundant robot is greater than the degree of freedom m required by the end effector to perform the expected task, which makes the redundant robot more flexible and fault-tolerant, and...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16
CPCB25J9/1664
Inventor 孙明轩张钰吴雨芯
Owner ZHEJIANG UNIV OF TECH
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