融合视觉与力的机器人反馈控制文献综述

文献综述

Introduction: The topic that I will be working on is a scientific research project, mainly to design and realize the shaft hole assembly simulation of vision and force feedback of the joint virtual simulation platform CoppeliaSim and Python. Shaft can be defined as it is a member which can fit into another part with proper assembly of the part. A shaft can either rotate or be stationary. The shaft will be applied to a member which has to fit even if there is a restriction between two spaces.Hole can be defined as a member who can house or fit the shaft. A hole can also rotate or be stationary. A hole can be applied to space between two restrictions in which a member has to fit. If an assembly is made of two parts whether both of them are similar or not similar, one part is known as a male part (outer elements of the part), and the other part is known as female part (inner element of the part) surface. The female part is known as the hole and the male part is known as the shaft.Here, my work is to assemble shaft and hole; and I have to design its simulation of vision force feedback of the joint virtual simulation platform CoppeliaSim and Python. The subject needs to design a type of manipulator shaft hole assembly simulation system based on vision-force feedback. The assembly process is divided into three stages: shaft hole coarse positioning, shaft hole fine positioning, and position adjustment in the hole, combined with monocular vision sensors and six The Weili sensor senses external environment information and workpiece information, and finally adopts the admittance feedback control strategy to adjust the posture of the robot end. This topic describes the construction of virtual multi-sensor data acquisition, processing and control strategies, which enables the realization of high-precision shaft hole assembly experiments. Based on actual needs, examine students independent design ability, analysis ability and practical ability of complex engineering.Figure: Cylindrical Shaft in Hole Assembly(a) The grasp of a shaft. (c) The axis pose estimation for the hole. (c) The smooth admittance of the shaft into the hole.Research design and planning: Im going to have to use CoppeliaSim; formerly known as V-REP, is a robot simulator used in industry, education and research. I will use it in order to build shaft hole assembly simulation and then I will use Python programming to perform the simulation. I have to use vision sensors. There is an example given:Fig: Using Vision Sensor Fig: Python Robot Simulation using CoppeliaSim It focuses on the problem of Robot control based on vision and force fusing, especially for the task of problem solving. Propose effective algorithms based on low-rank matrix recovery for image restoration. It is a study on the Shaft hole assembly problem and learn about the state-of-the-art methods in this area. It is based on existing methods, develop a Robot control based on vision and force fusing simulation. It performs experiments using the developed algorithm, compare it with existing algorithms. My proposal: For the whole project I used simulation of Shaft Hole Assembly Robot using CoppeliaSim and Pycharm for Python coding; It is going to be a simulation of Vision and Force feedback.Its going to follow shaft hole coarse positioning, shaft hole fine positioning, position adjustment in the hole combined with monocular vision sensors and six the Weili sensor which senses external environment information and workpiece information and finally adopts the admittance feedback control strategy to adjust the posture of the robot end. It is a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, I employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In this method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

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