Delta robot locus programming method based on gravitation searching particle swarm algorithm

Abstract

The invention provides a Delta robot locus programming method based on a gravitation searching particle swarm algorithm, comprising: employing a grid division mode to segment a Delta robot main conveyor belt operation area into M*N grids and determining a standard point coordinate; constructing a Cartesian space door-shaped grabbing locus, and deriving the kinematic inverse solution of each driving shaft angle and a rear end performer position; employing a gravitation searching particle swarm algorithm, using time optimum as a fitness function, and under the condition of meeting continuous and smooth joint speed, acceleration and pulsation and not exceeding a servo motor rated restriction value, constructing seven non-uniform rational B-spline functions, and obtaining an off line interpolation joint space angle-time node sequence; and after the Delta robot identifying a target point coordinate, calculating a joint space angle discrete sequence, querying data off-line stored in a three-dimensional array, obtaining a time node sequence, programming a motion locus and completing specified motion.

Description

technical field [0001] The invention relates to the field of trajectory planning of a Delta robot, in particular to a trajectory planning method for a Delta robot based on a gravitational search particle swarm algorithm. Background technique [0002] In the industrial field, only the starting point and end point of the robot movement are given, and the robot can complete the corresponding action. In order to ensure the flexibility of the movement between the start and end points of the robot, reduce the wear of the mechanical structure, and move quickly under the premise of meeting the specifications of the servo motor, it is necessary to plan the trajectory of the movement process of each joint of the robot. [0003] At present, trajectory planning schemes can be divided into energy-optimized, time-optimized, and minimum-pulsation types according to different optimization objectives. Among them, the trajectory planning with optimal time and minimum pulsation can meet the n...

AI Technical Summary

Problems solved by technology

Due to the complexity of the above-mentioned optimization algorithm and the constraints of the hardware computing speed at this stage, real-time performance has become the bottleneck for the application of this algorithm to industrial robots.

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