Time optimal trajectory planning method for robotic arm

Abstract

The present invention provides a time optimal trajectory planning method for a robotic arm, which has the following features: Step 1: discretizing a task space of the robotic arm into a plurality of trajectory difference points to obtain a pose matrix sequence Ti and a corresponding time node sequence ti, and performing an inverse operation on the pose matrix sequence Ti to obtain a corresponding joint position qi, wherein the relationship between the joint position qi and the time node sequence ti is as shown in the formula S = (qi, ti); Step 2: setting kinetics speed constraints; Step 3: according to the kinematic speed constraints, connecting points corresponding to each S in turn through the cubic polynomial curve interpolation to obtain a joint trajectory curve of the robotic arm; Step 4: establishing a target function according to the joint trajectory curve of the robotic arm; and Step 5: according to the target function, using a multi-population genetic algorithm to optimize the total movement time of the robotic arm to obtain a movement speed curve, a joint acceleration curve and a joint movement pulse curve of the robotic arm.

Description

technical field [0001] The invention relates to a method for time-optimized trajectory planning of a mechanical arm. Background technique [0002] With the development of science and technology, the mechanical arm has penetrated into all walks of life. In its practical application, the working accuracy and motion stability are important indicators to measure the working performance of the mechanical arm. On this basis, improving work efficiency, reducing operating errors, reducing running time and energy consumption have become key issues to be solved in the application of robotic arms. The premise and basis of solving these problems is to study the trajectory planning of the manipulator. There are many performance optimization indexes for the trajectory planning algorithm of the manipulator, such as time optimization and energy optimization, among which time optimization is currently the most researched. The use of intelligent optimization algorithms for time performance ...

AI Technical Summary

Problems solved by technology

[0004] However, for the trajectory planning problem of the manipulator, the genetic algorithm (SGA) still has many problems, such as premature convergence and low optimization efficiency.

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