A swarm intelligence calculation method for manipulator motion planning

Abstract

The invention discloses an intelligent calculating method for a mechanical arm movement planning group, which comprises the following steps: step 1, constructing a three-dimensional coordinate systemof the working space of the mechanical arm; step 2, constructing a target recognition model of an object to be captured based on the fuzzy neural network; step 3, constructing a mechanical arm based on the limit learning machine to grab an optimal path model; step 4, acquiring object image recognition object categories to be captured in real time; step 5; determining the center of gravity of the object based on the object category, obtaining the coordinate of the grabbing end point, acquiring the optimal path of the mechanical arm, and driving the mechanical arm to grab the object; The methodadopts an intelligent algorithm to calculate an ideal track with free collision, dynamic characteristic meeting allowance requirements and short track length and time for a mechanical arm grabbing target object, it greatly improves the production efficiency, saves the personnel cost and brings benefits to the factory.

Description

technical field [0001] The invention belongs to the field of manipulator control calculations, in particular to a group intelligent calculation method for manipulator motion planning. Background technique [0002] With the advent of "Made in China 2025", intelligent robots such as manipulators, AGVs, drones, etc. will undertake more and more work in the manufacturing environment. The existing mechanical arm can be controlled by the teaching pendant and the host computer. The teaching pendant control is to control the joints of the mechanical arm through the joystick and buttons on the teaching pendant, and then store the program after completing the predetermined working conditions; the host computer control It is programmed in the upper computer, which can complete the cycle of the predetermined working conditions. The above two manipulator control methods can only complete the cycle of fixed working conditions, and cannot cope with changes in working conditions. For the e...

AI Technical Summary

Problems solved by technology

The above two manipulator control methods can only complete the cycle of fixed working conditions, and cannot cope with changes in working conditions. For the existing manipulator operation, the field of view of the manipulator is limited through the monitoring device, and the operator is very prone to accidents during the entire control process. Unbearable biological fatigue leads to mistakes and safety accidents. For this reason, the automatic recognition of the robotic arm and the design of the grasping object path are a major breakthrough in solving the problem of robotic arm control.

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