The study on SCARA robot arm for working on 3D route optimization

• Main Authors: Tzu-Cheng Kuo, 郭子正
• Other Authors: Wen-June Wang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/8f5n7r

碩士 === 國立中央大學 === 電機工程學系 === 105 === In the modern community, various robot arms are widely used in different industrial areas. In general, the engineer manually set target point and pathway via the computer in advanced so that the robot arm can strictly move along with the preset path. If there exists a problem of visiting multiple targets, the preset path cannot be ensured that is the best arrangement. In order to improve the production efficiency, the moving cost of the robot arm needs to be reduced to carry out our study purpose. In this paper, we will present an experiment of locking screws to clearly introduce our proposed algorithm. Each point needs to be passed only once for the overall experimental procedure. Obviously, this is a typical travelling salesman problem (TSP). To be honest, the previous algorithms can only solve some problems in 2D space. However, this study is applied to cope with the issue of optimal path of locking screws in 3D space by the Selective Compliance Assembly Robot Arm (SCARA). The heights of screw holes are different when the detected object is 3D structure. Then, it is observed that the path between two points may be obstructed by certain obstacles to seriously disturb the normal work of a robot arm. In order to solve this problem, Kinect is used to build the 3D model of the object and simultaneously detect screw holes based on the image processing method. To avoid the robot arm hitting the obstacle on the path, we have to utilize avoiding algorithm to stop this situation appearance. The avoiding algorithm based on the visibility graph algorithm has been proposed in this study in order to let the general visibility graph algorithm transfer to 3D space. Namely, we propose a strategy to add new nodes so that it can help us easily calculate the moving cost of each point. Substituting the moving cost into TSP to calculate the optimal path where the TSP problem can be solved by the Particle Swarm Optimization (PSO) and 2-Opt algorithms. Finally, transferring the image coordinate to SCARA coordinate to let the robot arm complete the whole task.