Visual Servoing in Robotics

Visual servoing is a well-known approach to guide robots using visual information. Image processing, robotics, and control theory are combined in order to control the motion of a robot depending on the visual information extracted from the images captured by one or several cameras. With respect to vision issues, a number of issues are currently being addressed by ongoing research, such as the use of different types of image features (or different types of cameras such as RGBD cameras), image processing at high velocity, and convergence properties. As shown in this book, the use of new control schemes allows the system to behave more robustly, efficiently, or compliantly, with fewer delays. Related issues such as optimal and robust approaches, direct control, path tracking, or sensor fusion are also addressed. Additionally, we can currently find visual servoing systems being applied in a number of different domains. This book considers various aspects of visual servoing systems, such as the design of new strategies for their application to parallel robots, mobile manipulators, teleoperation, and the application of this type of control system in new areas

Generating Optimized Trajectories for Robotic Spray Painting

In the manufacturing industry, spray painting is often an important part of the manufacturing process. Especially in the automotive industry, the perceived quality of the final product is closely linked to the exactness and smoothness of the painting process. For complex products or low batch size production, manual spray painting is often used. But in large scale production with a high degree of automation, the painting is usually performed by industrial robots. There is a need to improve and simplify the generation of robot trajectories used in industrial paint booths. A novel method for spray paint optimization is presented, which can be used to smooth out a generated initial trajectory and minimize paint thickness deviations from a target thickness. The smoothed out trajectory is found by solving, using an interior point solver, a continuous non-linear optimization problem. A two-dimensional reference function of the applied paint thickness is selected by fitting a spline function to experimental data. This applicator footprint profile is then projected to the geometry and used as a paint deposition model. After generating an initial trajectory, the position and duration of each trajectory segment are used as optimization variables. The primary goal of the optimization is to obtain a paint applicator trajectory, which would closely match a target paint thickness when executed. The algorithm has been shown to produce satisfactory results on both a simple 2-dimensional test example, and a non-trivial industrial case of painting a tractor fender. The resulting trajectory is also proven feasible to be executed by an industrial robot

Optimal Trajectory Planning for Spray Coating

The problem of how to optimally traverse a spray applicator around a surface to be coated is formulated as a type of optimization problem known as a constrained variational problem. An optimal trajectory for a spray applicator is defined to be one that results in minimal variation in accumulated film thickness on the surface. The trajectory for an applicator is characterized by a six-dimensional vector function that specifies the position and orientation of the applicator at each instant of time. The surface to be coated is represented with a function. For each surface point and fclr each feasible position and orientation of the applicator, a value for the instantaneous rate of film accumulat\u27lon is assumed to be known. Empirical data and/or estimates for these values can be readily incorporated in the formulation. By making realistic approximations, the proposed constrained variational problem is transformed into a finite dimensional constrained optimization problem. Numerical studies are included that illustrate the utility of the problem formulation and the effectiveness of applying standard nonlinear programming techniques for determining solutions

Multiscale Design And Life-Cycle Based Sustainability Assessment Of Polymer Nanocomposite Coatings

In recent years, nanocoatings with exceptionally improved and new performance properties have found numerous applications in the automotive, aerospace, ship-making, chemical, electronics, steel, construction, and many other industries. Especially the formulations providing multiple functionalities to cured paint films are believed to dominate the coatings market in the near future. It has shifted the focus of research towards building sustainable coating recipes which can deliver multiple functionalities through applied films. The challenge to this exciting area of research arrives from the insufficient knowledge about structure-property correlations of nanocoating materials and their design complexity. Experimental efforts have been successful in developing certain types of nanopaints exhibiting improved properties. However, multifunctional nanopaint design optimality is extremely difficult to address if not impossible solely through experiments. In addition to this, the environmental implications and societal risks associated with this growing field of nanotechnology raise several questions related to its sustainable development. This research focuses on the study of a multiscale sustainable nanocoating design which can have the application from novel function envisioning and idea refinement point of view, to knowledge discovery and design solution derivation, and further to performance testing in industrial applications. The nanocoating design is studied using computational simulations of nano- to macro- scale models and sustainability assessment study over the life-cycle. Computational simulations aim at integrating top-down, goals/means, inductive systems engineering and bottom-up, cause and effect, deductive systems engineering approaches for material development. The in-silico paint resin system is a water-dispersible acrylic polymer with hydrophilic nanoparticles incorporated into it. The nano-scale atomistic and micro-scale coarse-grained (CG) level simulations are performed using molecular dynamics methodology to study several structural and morphological features such as effect of polymer molecular weight, polydispersity, rheology, nanoparticle volume fraction, size, shape and chemical nature on the bulk mechanical and self-cleaning properties of the coating film. At macro-scale, a paint spray system which is used for automotive coating application is studied by using CFD-based simulation methodology to generate crucial information about the effects of nanocoating technology on environmental emissions and coating film quality. The cradle-to-grave life-cycle based sustainability assessment study address all the critical issues related to economic benefits, environmental implications and societal effects of nanocoating technology through case studies of automotive coating systems. It is accomplished by identifying crucial correlations among measurable parameters at different stages and developing sustainability indicator matrices for analysis of each stage of life-cycle. The findings from the research can have great potential to draft useful conclusions in favor of future development of coating systems with novel functionalities and improved sustainability

2차원 균일 커버리지 경로 계획을 위한 효율적 알고리즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 박종우.Coverage path planning (CPP) is widely used in numerous robotic applications. With progressively complex and extensive applications of CPP, automating the planning process has become increasingly important. This thesis proposes an efficient CPP algorithm based on a random sampling scheme for spray painting applications. We have improved on the conventional CPP algorithm by alternately iterating the path generation and node sampling steps. This method can reduce the computational time by reducing the number of sampled nodes. We also suggest a new distance metric called upstream distance to generate reasonable path following given vector field. This induces the path to be aligned with a desired direction. Additionally, one of the machine learning techniques, support vector regression (SVR) is utilized to identify the paint distribution model. This method accurately predict the paint distribution model as a function of the painting parameters. We demonstrate our algorithm on several types of analytic surfaces and compare the results with those of conventional methods. Experiments are conducted to assess the performance of our approach compared to the traditional method.본 논문에서는 2차원 표면의 균일 커버리지 경로 계획을 설명하고 이를 효율적으로 푸는 알고리즘을 제시한다. 우리는 경로 계획 문제를 두 개의 하위 문제로 분리하여 각각 푸는 기존의 방식을 보완하여 두 개의 하위문제를 한 번에 풀면서 계산시간을 줄이는 방법을 제시하였다. 또한 경우에 따라 주어진 벡터 필드와 나란한 방향으로 경로가 생성될 필요가 있는데 이를 위해 거스름 거리(upstream distance)의 개념을 제시하였으며 여행 외판원 문제(Traveling Salesman Problem)를 풀 때 이를 적용하였다. 우리는 차량 도장 응용분야에 균일 커버리지 경로 계획법을 적용하였으며 도장 시스템을 고려하여 균일한 페인트 두께를 보장하는 방법을 같이 제시하였다. 네 가지 타입의 2차원 곡면에 대해 시뮬레이션을 진행하였으며 기존의 방법에 비해 더 적은 계산시간을 요구하면서도 합리적인 수준의 페인트 균일도를 달성함을 검증하였다.1 Introduction 1 1.1 Related Work 3 1.2 Contribution of Our Work 7 1.3 Organization of This Thesis 8 2 Preliminary Background 9 2.1 Elementary Differential Geometry of Surfaces in R3 10 2.1.1 Representation of Surfaces 10 2.1.2 Normal Curvature 10 2.1.3 Shape Operator 12 2.2 Traveling Salesman Problem 15 2.2.1 Definition 15 2.2.2 Variations of the TSP 17 2.2.3 Approximation Algorithm for TSP 19 2.3 Path Planning on Vector Fields 20 2.3.1 Randomized Path Planning 20 2.3.2 Upstream Criterion 20 2.4 Support Vector Regression 21 2.4.1 Single-Output SVR 21 2.4.2 Dual Problem of SVR 23 2.4.3 Kernel for Nonlinear System 25 2.4.4 Multi-Output SVR 26 3 Methods 29 3.1 Efficient Coverage Path Planning on Vector Fields 29 3.1.1 Efficient Node Sampling 31 3.1.2 Divide and Conquer Strategy 32 3.1.3 Upstream Distance 34 3.2 Uniform Coverage Path Planning in Spray Painting Applications 35 3.2.1 Minimum Curvature Direction 35 3.2.2 Learning Paint Deposition Model 36 4 Results 38 4.1 Experimental Setup 38 4.2 Simulation Result 41 4.3 Discussion 41 5 Conclusion 45 Bibliography 47 국문초록 52Maste

Motion Planning of Redundant Manipulators for Painting Uniform Thick Coating in Irregular Duct

The paper presents a motion planning method of redundant manipulator for painting uniform thick coating on the interior of irregular duct of some aircrafts. Discontinuous peripheral painting method is employed by analyzing the restrictions during painting the duct. For improving the longitudinal uniformity of thick coating, the interlacing painting method plans two sets of sweeping paths and an interlacing distance between the starting paths of the two times of painting. The interlacing distance and overlapping distance are optimized by establishing and analyzing the model of longitudinal uniformity. To enhance the transverse uniformity, sweeping speeds for curved surfaces are calculated by the ratio of transfer efficiency after the basic sweeping speed for the plane is determined. The intertwining method, minimizing the sum of the weighed distances between the duct centerline and key points of the manipulator links, is employed for the joint trajectory planning without collision. The simulation and experiment results show that the redundant manipulators can finish painting the internal surface of the irregular S-shaped duct without collision. The maximum relative deviation is 16.3% and the thicknesses of all measurement points satisfy the acceptance criteria of the factory

Estimation and Control of Robotic Radiation-Based Processes

This dissertation presents a closed-loop control and state estimation framework for a class of distributed-parameter processes employing a moving radiant actuator. These radiation-based processes have the potential to significantly reduce the energy consumption and environmental impact of traditional industrial processes. Successful implementation of these approaches in large-scale applications requires precise control systems. This dissertation provides a comprehensive framework for: 1) integration of trajectory generation and feedback control, 2) online distributed state and parameter estimation, and 3) optimal coordination of multiple manipulated variables, so as to achieve elaborate control of these radiation-based processes for improved process quality and energy efficiency. The developed framework addresses important issues for estimation and control of processes employing a moving radiant actuator from both practical and theoretical aspects. For practical systems, an integrated motion and process control approach is first developed to compensate for disturbances by adjusting either the radiant power of the actuator or the speed of the robot end effector based on available process measurements, such as temperature distribution. The control problem is then generalized by using a 1D scanning formulation that describes common characteristics of typical radiant source actuated processes. Based on this 1D scanning formulation, a distributed state and parameter estimation scheme that incorporates a dual extended Kalman filter (DEKF) approach is developed to provide real-time process estimation. In this estimation scheme, an activating policy accompanying the moving actuator is applied in order to reduce the computational cost and compensate for observability changes caused by the actuator\u27s movement. To achieve further improvements in process quality, a static optimization and a rule-based feedback control strategy are used to coordinate multiple manipulated variables in open-loop and closed-loop manners. Finally, a distributed model predictive control (MPC) framework is developed to integrate process optimization and closed-loop coordination of manipulated variables. Simulation studies conducted on a robotic ultraviolet (UV) paint curing process show that the developed estimation and control framework for radiant source actuated processes provide improved process quality and energy efficiency by adaptively compensating for disturbances and optimally coordinating multiple manipulated variables

Computer Aided Design of a Low-Cost Painting Robot

The application of robots or robotic systems for painting parts is becoming increasingly conventional; to improve reliability, productivity, consistency and to decrease waste. However, in Pakistan only highend Industries are able to afford the luxury of a robotic system for various purposes. In this study we propose an economical Painting Robot that a small-scale industry can install in their plant with ease. The importance of this robot is that being cost effective, it can easily be replaced in small manufacturing industries and therefore, eliminate health problems occurring to the individual in charge of painting parts on an everyday basis. To achieve this aim, the robot is made with local parts with only few exceptions, to cut costs; and the programming language is kept at a mediocre level. Image processing is used to establish object recognition and it can be programmed to paint various simple geometries. The robot is placed on a conveyer belt to maximize productivity. A four DoF (Degree of Freedom) arm increases the working envelope and accessibility of painting different shaped parts with ease. This robot is capable of painting up, front, back, left and right sides of the part with a single colour. Initially CAD (Computer Aided Design) models of the robot were developed which were analyzed, modified and improved to withstand loading condition and perform its task efficiently. After design selection, appropriate motors and materials were selected and the robot was developed. Throughout the development phase, minor problems and errors were fixed accordingly as they arose. Lastly the robot was integrated with the computer and image processing for autonomous control. The final results demonstrated that the robot is economical and reduces paint wastage