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

Trajectory and spray control planning on unknown 3D surfaces for industrial spray painting robot

Automated 3D path and spray control planning of industrial painting robots for unknown target surfaces is desired to meet demands on the production system. In this thesis, an image acquisition and laser range scanning based method has been developed. The system utilizes the XY projection of the boundaries of the target surface to generate the gun trajectory\u27s X and Y coordinates as well as the spray control. Z coordinates and gun direction, distance, and speed are generated based on the point cloud from the target that is acquired by the laser scanner. A simulation methodology was also developed which is capable of calculating the paint thickness across the target surface. Results have shown that the generated path could perform a full coverage on the target surface, while keeping the paint material waste at the minimum. Excellent paint thickness control could be achieved on 2D and straight line sweep surfaces, while a satisfactory thickness is obtained on other 3D arbitrary surfaces. Relationships among thickness, spray deposition profile, sampling roughness and geometric features of the target surfaces have been discussed to make this method more applicable in industry

An Integrated Method for the Geometric Inspection of Wind Turbine Hubs with Industrial Robot

Wind turbine manufacturing requires the assembly of large mechanical components, which is crucial to inspect along the production line in order to prevent high reparation costs afterwards. A critical component in this process is the turbine hub, which supports the wind blades and ball bearings allowing the pitch motion. At present, hub inspection is a manual task, which requires expert operators and long execution time. This paper proposes a novel methodology for the selfadaptive inspection of wind turbine hubs via industrial robots: a set of Critical-To-Quality parameters (CTQs), are inferred from the CAD drawing of wind turbine hub; registration between robot and hub is performed; finally a CAD2robot trajectories planning is accomplished. Methodology is implemented through a Matlab and Simulink Programming Language and combined with an Industrial PC-based control technology Beckhoff TwinCAT 3. Tests with an Fanuc Industrial M-6iB robot arm and R-30iA controller have been successfully performed on re-scaled model of the hub. The flexibility of this methodology allows applications on other industrial contexts, which can benefit from automation

Direct off-line robot programming via a common CAD package

This paper focuses on intuitive and direct off-line robot programming from a CAD drawing running on a common 3-D CAD package. It explores the most suitable way to represent robot motion in a CAD drawing, how to automatically extract such motion data from the drawing, make the mapping of data from the virtual (CAD model) to the real environment and the process of automatic generation of robot paths/programs. In summary, this study aims to present a novel CAD-based robot programming system accessible to anyone with basic knowledge of CAD and robotics. Experiments on different manipulation tasks show the effectiveness and versatility of the proposed approach

Collaborative Robotic Path Planning for Industrial Spraying Operations on Complex Geometries

Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists of key modules such as a path planner, path simulator, and result simulator. An integrated user interface facilitates the operator to interact with these modules and edit the path plan before ultimately approving the task for automatic execution by a manipulator that need not be collaborative. Application of the collaborative framework is illustrated for a pressure washing task in a remanufacturing environment that requires one-off path planning for each part. The framework can also be applied to various other tasks, such as spray-painting, sandblasting, deburring, grinding, and shot peening. Specifically, automated path planning for industrial spraying operations offers the potential to automate surface preparation and coating in such environments. Autonomous spray path planners in the literature have been limited to generally continuous and convex surfaces, which is not true of most real parts. There is a need for planners that consistently handle concavities and discontinuities, such as sharp corners, holes, protrusions or other surface abnormalities when building a path. The path planner uses a slicing-based method to generate path trajectories. It identifies and quantifies the importance of concavities and surface abnormalities and whether they should be considered in the path plan by comparing the true part geometry to the convex hull path. If necessary, the path is then adapted by adjusting the movement speed or offset distance at individual points along the path. Which adaptive method is more effective and the trade-offs associated with adapting the path are also considered in the development of the path planner

Development of discontinuous fibre preforming processes

Discontinuous fibre composites are under increasing investigation for structural and semi-structural components as they are easily automated, making it possible to remove costly hand labour based steps typically associated with advanced fibre reinforced composites. Directed fibre preforming (DFP) is one possible process which has several advantages when compared with competing techniques. Low material and process costs coupled with short cycle times means the process is suited to medium volume production (typically <10,000 ppa). Predicting mechanical performance remains a major obstacle to industrial adoption however, due to the stochastic nature of fibre distribution. This is of particular importance for structural applications where minimum property requirements and a greater certainty of performance must be achieved. This thesis employs a stochastic macroscale modelling approach to predict fibre locations during the reinforcement deposition stage. This is achieved through process characterisation studying the effects of key microstructural and process-specific parameters on fibre distribution and orientation. The proposed DFP simulation software can generate realistic fibre networks for complex three-dimensional component geometries providing feedback on preform quality. This information is used to optimise the preform structure via process input parameters such as robot trajectory and material properties with validation tests conducted to assess model accuracy. An interface between the simulation software and commercial finite element code facilitates mechanical property analysis for full-scale components using realistic load cases. The complete software package is intended to streamline the route to manufacture for DFP processes from a conceptual design stage

PATH PLANNING FOR AUTOMATED ROBOT PAINTING

ABSTRACT This paper describes the analysis work underlying the pathplanning algorithm for a robotic painting system. The system requires no bespoke production tooling and fills an automation gap in rapid prototyping and manufacturing technology that is currently occupied by hand painting. The system creates images by exposing individual pixels of a photographic coating with a robot-mounted laser. The painting process requires no physical contact so potentially images could be developed on any shape regardless of its complexity: As objects can only be &quot;painted&quot; when their surface can be &quot;hit&quot; (i.e. exposed) by the light beam the system requires six degrees of freedom to ensure all overhanging or reentrant areas can be exposed. The accuracy of serial robots degrades with the length of the kinematic chain (in other words six axis robots cannot position themselves with the same accuracy as four axis ones). Consequently to ensure high precision in the location and orientation of the light source, the object being exposed is mounted on a rotary tilt table within the workspace of a fouraxis robot. This gives a six-degree of freedom positioning system composed of two separate kinematic chains. Although the resulting system is accurate the problems of constructing a coordinated path that allows the light beam to efficiently sweep (i.e. cover) the surface regardless of its geometry are challenging. This paper describes the difficulties and, after reviewing existing path planning algorithms, a new algorithm is introduced firstly by describing the nature of the system&apos;s configuration space and then further developing this concept as an alternative to a previously described planning algorithm. Having outlined the approach the paper presents a kinematic model for the system and compares the configuration space approach to a purely Cartesian planning approach

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task. Yet, existing solutions make strong assumptions on the form of input surfaces and the nature of output paths, resulting in limited approaches unable to cope with real-data variability. By leveraging on recent advances in 3D deep learning, we introduce a novel framework capable of dealing with arbitrary 3D surfaces, and handling a variable number of unordered output paths (i.e. unstructured). Our approach focuses on predicting smaller path segments, which can be later concatenated to reconstruct long-horizon paths. We extensively validate the proposed method in the context of robotic spray painting by releasing PaintNet, the first public dataset of expert demonstrations on free-shape 3D objects collected in a real industrial scenario. A thorough experimental analysis demonstrates the capabilities of our model to promptly predict smooth output paths that cover up to 95% of the surface of previously unseen object instances. Furthermore, we show how models learned from PaintNet capture relevant features which serve as a reliable starting point to improve data and time efficiency when dealing with new object categories