US 11,938,636 B2
Feature-guided scanning trajectory optimization method for three-dimensional measurement robot
Jun Wang, Nanjing (CN); Hangbin Zeng, Nanjing (CN); Yuanpeng Liu, Nanjing (CN); Zhengshui Kang, Nanjing (CN); and Jianping Yang, Nanjing (CN)
Assigned to Nanjing University of Aeronautics and Astronautics, Nanjing (CN)
Filed by Nanjing University of Aeronautics and Astronautics, Nanjing (CN)
Filed on May 23, 2023, as Appl. No. 18/321,840.
Prior Publication US 2023/0311319 A1, Oct. 5, 2023
Int. Cl. B25J 9/16 (2006.01); G01B 21/20 (2006.01); B25J 19/02 (2006.01)
CPC B25J 9/1664 (2013.01) [G01B 21/20 (2013.01); B25J 19/02 (2013.01)] 8 Claims
OG exemplary drawing
 
1. A feature-guided scanning trajectory optimization method for a three-dimensional (3D) measurement robot, comprising:
(S1) building a 3D digital model of an aircraft surface; obtaining a size of the 3D digital model; extracting features to be measured from the 3D digital model; and classifying the features to be measured;
(S2) calculating a geometric parameter of each type of features to be measured; and generating an initial scanning trajectory of each type of features to be measured;
(S3) building a constraint model of the 3D measurement robot; and optimizing the initial scanning trajectory of each type of features to be measured into a local optimal scanning trajectory; and
(S4) based on the local optimal scanning trajectory, planning a global optimal scanning trajectory of the features to be measured of the aircraft surface by using a modified ant colony optimization algorithm;
wherein the step (S3) comprises:
(S31) based on a flange coordinate system of the 3D measurement robot, converting a posture change of the 3D measurement robot during a scanning process into changes of scanning depth d, yaw angle α, pitch angle β and rotation angle ω; and building the constraint model of the 3D measurement robot;
(S32) based on the constraint model of the 3D measurement robot, traversing initial scanning trajectories of all types of features to be measured; and constructing a trajectory optimization evaluating function E, expressed as:
E=5×10−5×√(1.2dmax−d)2×√(ϕ−0.6ϕmin)2+λ+μ√(φ−0.3φmin)2;
wherein ϕ is an out-of-plane angle; φ is an in-plane angle; λ is a penalty factor of the trajectory optimization evaluating function; μ is a penalty factor of the in-plane angle; dmax is a maximum scanning depth; and ϕmin is a minimum out-of-plane angle; and
(S33) repeating the step (S32) successively with the scanning depth, in-plane angle and out-of-plane angle as a single variable to minimize the trajectory optimization evaluating function E, so as to obtain the local optimal scanning trajectory of each type of features to be measured, expressed as:

OG Complex Work Unit Math
wherein li is an effective scanning width of a 3D measurement scanner; lmin is a width between the initial scanning trajectories; φmin represents a minimum in-plane angle; and φmax represents a maximum in-plane angle.