New design of a compact aero-robotic drilling end effector: An experimental analysis

This paper presents the development of a normal adjustment cell (NAC) in aero-robotic drilling to improve the quality of vertical drilling, by using an intelligent double-eccentric disk normal adjustment mechanism (2-EDNA), a spherical plain bearing and a floating compress module with sensors. After the surface normal vector is calculated based on the laser sensors' feedback, the 2-EDNA concept is conceived specifically to address the deviation of the spindle from the surface normal at the drilling point. Following the angle calculation, depending on the actual initial position, two precise eccentric disks (PEDs) with an identical eccentric radius are used to rotate with the appropriate angles using two high-resolution DC servomotors. The two PEDs will carry the spindle to coincide with the surface normal, keeping the vertex of the drill bit still to avoid repeated adjustment and position compensation. A series of experiments was conducted on an aeronautical drilling robot platform with a precise NAC. The effect of normal adjustment on bore diameter, drilling force, burr size, drilling heat, and tool wear was analyzed. The results validate that using the NAC in robotic drilling results in greatly improved vertical drilling quality and is attainable in terms of intelligence and accuracy. (C) 2015 The Authors. Production and hosting by Elsevier Ltd. on behalf of Chinese Society of Aeronautics and Astronautics.

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy,such as leaf area,leaf distribution,and 3D model.The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds.This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive,unorganized LiDAR point clouds.In order to mine the distinct geometry of corn leaves and stalk,the Difference of Normal(DoN)method was proposed to extract corn leaf points.Firstly,the normals of corn leaf surface for all points were estimated on multiple scales.Secondly,the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution.Finally,the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points.The quantitative accuracy assessment showed that the overall accuracy was 94.10%,commission error was 5.89%,and omission error was 18.65%.The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive,unorganized terrestrial LiDAR point clouds using the proposed DoN method.