|关键词||铝电解槽 图像分割 自适应灰度差法 先验知识 避障路径规划|
Autonomous intelligent grasping in cluttered scene is one of the key research areas for industry intelligence. Intelligent robot requires the ability to sense and understand the environment, plan a series of manipulation in complicated environment and accomplish the grasping task. The method for aluminium electrolytic cells’ repairment is mainly to carry the workpiece to connect the cathode steel bar and the explosive steel sheet, then to weld the seam between them by human beings. The environment in aluminium electrolytic cells is terrible, the work intensity is high, and it is difficult to ensure the welding quality and consistency. Thus, there is an urgent need for a special robot working in aluminium electrolytic cells to carry the workpieces to the destination and weld the seam. The environment of aluminium electrolytic cells is magnetically strong. The welding workpieces are various in shape, imprecisely placed, occluded by each other and reflective because they are the planar metal workpieces. There are many obstacles in the aluminium electrolytic cells, and the inclination for the surface of the explosive steel sheet changes dynamically because of the high temperature in the process of welding. There are also some small differences in different cells. All these difficulties are great challenges for the grasping in the aluminium electrolytic cells. According the problems mentioned above, the research of autonomous grasping based on vision in aluminium electrolytic cells’ repairment is conducted in this paper, and the methods are proposed. The main content of this paper is in the following.
Firstly, based on the characteristics of the transportation and welding in the aluminium electrolytic cells’ repairment and their narrow space, the dual arm welding robot is designed and the control system which is based on hierarchical control and modularity method is planned to ensure a high accurate and stable robot system.
Secondly, the inspect method of the grasping posture measurement for the metal polygon workpieces in simple environment is discussed. There exists reflection, scratch and rust on the surface of the metal workpiece, and the gray difference between the metal workpiece and the background is obvious, thus the self-adaptive gray difference method is used to detect the edge of the workpiece by the edge pixels’ scan, noise reduction and line fitting no matter of the light variation on the surface of the metal
workpiece. Meanwhile, the structured light vision system is used to measure the deep information of the workpiece and fit the maximum plane of the planar workpiece, and the vision localization and measurement for the convex polygon metal workpiece in the field of the aluminium electrolytic cells’ repairment is solved.
Thirdly, the image segmentation for metal workpieces in cluttered scene based on prior knowledge is proposed. The workpieces for the repairment of the aluminium electrolytic cells are various planar workpieces according to different situations. For different conditions of the cluttered scene, such as the reflection, the occlusion and complicated background, the shape prior, non-linear shape prior and fuzzy connectedness prior knowledge are applied to solve the image segmentation combined with the framework of GraphCuts method for the workpieces with different sizes, imprecisely placed and occlusion.
Fourthly, the collision avoidance path planning in narrow space is designed. The target workpiece is grasped by approaching it according to the image-based visual control. According to the cluttered scene in the aluminium electrolytic cells, the 3D environment model is constructed by the structured light vision system and the collision checking is executed by line segments' intersection detection. The probabilistic obstacle-constraint reaching-target path nodes generation method is proposed combined with the second-order Bezier curve interpolation method to accomplish the obstacle avoidance path planning, and reach the goal of grasping and carrying the workpiece stably and accurately. The experiments of approaching grasping and transportation of the workpieces validate the effectiveness of the obstacle avoidance path planning algorithm in the aluminium electrolytic cells.
|郑晶怡. 基于视觉的铝电解槽机器人自主抓取作业的关键技术研究[D]. 北京. 中国科学院研究生院,2016.|