To improve wood structure processing efficiency, a palletizing robot suitable for loading and unloading glued laminated timber (GLT) has been developed. The robot comprises a six-axis connecting rod mechanism and a sponge sucker as a grasping actuator, which can enable the intelligent automatic loading and unloading and palletizing operations for small-sized GLT. Matlab robotics was used to construct the kinematic model of the GLT loading and unloading robot. Based on Matlab and Monte Carlo methods, the robot workspace was simulated and analyzed to determine the scope of the robot workspace. Using the high-order quintic and sixtic polynomial curve interpolation method, the trajectory of wood structure parts in the process of loading and unloading operations was planned, respectively, under the two conditions of staying and not staying. Tests verified that the simulation results of the pose of the end-effector were consistent with the actual pose of the robot. The robot’s working range could be analyzed intuitively and effectively. The robot’s operation trajectory planning provides data support and a parameter basis for the automatic control and program design of a loading, unloading and palletizing robot.

In order to be utilized in the design of a wood building, the lumber must pass grade. Machine-vision inspection grading offers higher efficiency and accuracy than traditional manual visual grading. In this paper, a fast and accurate method for identifying defects in large-size structural lumber based on machine vision of Fujian Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) constructional lumber (FCF CL) is proposed. Specifically, the grey matrix of the captured images on the surface of the sawn timber is initially scanned and the pixel weights on the edges of the image greyness variables are calculated. A matrix-valued torus was formed by fitting the knot edge profile and analyzing changes in the gradient values at the knot's edge, as well as calculating the directional derivative's rate of change. The knot three-dimensional mapping curves were projected onto the plane to form horizontal rise contours. Observe from the contour map of the whole large-size sawn timber, and extract the positional information of the knot where there is a trough (groove). The test results show that the rRMSE (Relative Root Mean Square Error) measured at the x axis position of knots is within 0.49 percent; the rRMSE measured at the y axis is 0.35 percent, which has high detection accuracy and meets the production requirements. We also investigated the effect of knots in different positions on the modulus of elasticity and the bending strength of FCF CL, with a view to establishing a link between machine-vision knot detection and mechanical properties of sawn timber in our next work.