Cooperative trajectory optimization method for dual robot based on parabolic interpolation algorithm

Abstract

The invention discloses a cooperative trajectory optimization method for a dual robot based on a parabolic interpolation algorithm. In an effective working space of the dual robot cooperative work andunder certain constraints, the optimization variables are selected to determine a feasible domain thereof. Based on an idea of motion stability, the parabolic interpolation algorithm is used to search for an optimal value of the optimization variables, and then an optimal cooperative trajectory is obtained. During a trajectory planning, each motion path point is firstly transformed into a joint path point by using an inverse kinematics equation, and then a fitting smooth function is performed by the joint path point corresponding to each joint. These joint trajectory functions respectively indicate that the trajectories of the joints from a starting position, pass through all the path points, and finally arrive the destination.

Description

technical field [0001] The invention belongs to the field of robot control, and belongs to the aspect of robot trajectory optimization, in particular to a dual-robot collaborative trajectory optimization method based on a parabolic interpolation algorithm. . Background technique [0002] Trajectory planning is the basis of robot control. The quality of trajectory planning directly affects the working efficiency and motion stability of the robot, especially in the fields of robot welding, grinding, and cutting. The trajectory generation of the robot involves the mapping from the task space to the joint space, and the mapping is nonlinear, so the trajectory planning of the robot is more complicated. [0003] When trajectory planning is performed in the joint space, the time function is used to describe the joint variable, and the first and second derivatives of the joint variable time function are planned. Since the trajectory of the robot is only expressed by the function o...

AI Technical Summary

Problems solved by technology

Comparing the quintic polynomial interpolation algorithm and the cubic polynomial interpolation algorithm in the joint space, the advantage of the cubic polynomial interpolation algorithm is that it can ensure continuous joint angles and angular velocities when implementing trajectory planning, and it only needs a quaternary linear equation The calculation amount is relatively small, but its shortcomings are also very obvious. Since the joint angular acceleration constraint is not used, the continuity of the joint angular acceleration cannot be guaranteed, which may cause the motor at the joint to vibrate.

The quintic polynomial interpolation algorithm considers the joint acceleration constraints during trajectory planning, so it can ensure the continuity of each joint value at each node, so that the motor runs smoothly, but compared with the cubic polynomial, its calculation amount will be slightly bigger

[0004] When using the quintic polynomial difference algorithm or the cubic polynomial difference algorithm, there will be motion instability, the main reason is that the derivative of the interpolation function tends to infinity, that is, the joint acceleration tends to infinity.

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