Coordinated mechanical arm multi-constraint processing optimization method based on speed redistribution

Abstract

The invention discloses a coordinated mechanical arm multi-constraint processing optimization method based on speed redistribution. The method comprises the following steps that S1, a mechanical arm coordinated clamping kinetic model is constructed, a physical model of a multi-mechanical arm clamping workpiece is simplified, an inertia term, a Coriolis force term, a centrifugal force item and a gravity term in the mechanical arm kinetic model are determined through a Lagrangian kinetic method, and an internal force item of the workpiece and a calculation method thereof are introduced into thekinetic equation to form an equation; and S2, a joint speed redistribution algorithm of a coordinated mechanical arm under the constraint condition is designed based on a homogeneous weighted minimumnorm method, a multi-mechanical arm coordinated task Jacobian matrix is constructed firstly, then a performance index equation based on joint position and joint moment constraint is designed, and based on the performance index equation as well as combined with a mechanical arm kinematics and a kinetic flush configuration, a speed distribution expression based on joint penalty weights is designed.

Description

technical field [0001] The invention relates to a multi-manipulator processing optimization method, in particular to a speed reconfiguration-based multi-constraint processing optimization method for coordinated manipulators. Background technique [0002] In the current processing and manufacturing industry, such as grinding, handling, spraying and welding, more and more multi-manipulator arms are required to perform complex interactive and collaborative tasks, especially in some special environments and high-precision energy-saving fields. , although the traditional single manipulator can meet the basic task requirements, it needs to be improved in terms of work efficiency and completion quality. Therefore, multi-manipulator cooperative processing is particularly important. However, for the joint position constraints and joint torque constraints of multi-manipulator cooperation in a specific complex environment, the current practical application is still in the manual teachi...

AI Technical Summary

Problems solved by technology

[0002] In the current processing and manufacturing industry, such as grinding, handling, spraying and welding, more and more multi-manipulator arms are required to perform complex interactive and collaborative tasks, especially in some special environments and high-precision energy-saving fields. , although the traditional single manipulator can meet the basic task requirements, it needs to be improved in terms of work efficiency and completion quality. Therefore, multi-manipulator cooperative processing is particularly important

However, for the joint position constraints and joint torque constraints of multi-manipulator cooperation in a specific complex environment, the current practical application is still in the manual teaching stage, and each joint is generally taught individually according to the on-site environment of the manipulator. , to avoid joint obstacles, and did not use the speed reconfiguration algorithm to replan the trajectory according to the acquired environmental conditions

At the level of torque constraints, many current applications provide a large amount of torque redundancy, which undoubtedly increases energy consumption and wastes resources. When it comes to special application scenarios such as aerospace that require low energy consumption, it must Strict consideration of joint torque selection

Moreover, in the current theoretical research, many studies focus on the joint position constraints at the kinematic level of the manipulator, or only consider the moment constraints of the manipulator joints based on the acceleration level. In both cases, the position and torque are not fully considered at the same time. Constraint problems, resulting in a separation of both kinematics and dynamics

It can be found that re-planning the acceleration in the lower acceleration layer will lead to a decrease in computational efficiency

Images