Painting Robot Research Articles

Automatic Optimal Robotic Base Placement for Collaborative Industrial Robotic Car Painting

This paper investigates the problem of optimal base placement in collaborative robotic car painting. The objective of this problem is to find the optimal fixed base positions of a collection of given articulated robotic arms on the factory floor/ceiling such that the possibility of vehicle paint coverage is maximized while the possibility of robot collision avoidance is minimized. Leveraging the inherent two-dimensional geometric features of robotic car painting, we construct two types of cost functions that formally capture the notions of paint coverage maximization and collision avoidance minimization. Using these cost functions, we formulate a multi-objective optimization problem, which can be readily solved using any standard multi-objective optimizer. Our resulting optimal base placement algorithm decouples base placement from motion/trajectory planning. In particular, our computationally efficient algorithm does not require any information from motion/trajectory planners a priori or during base placement computations. Rather, it offers a hierarchical solution in the sense that its generated results can be utilized within already available robotic painting motion/trajectory planners. Our proposed solution’s effectiveness is demonstrated through simulation results of multiple industrial robotic arms collaboratively painting a Ford F-150 truck.

Dry brush model for robotic painting

Dry brush model for robotic painting

Physically Motivated Model of a Painting Brush for Robotic Painting and Calligraphy

Robot artistic painting and robot calligraphy do require brush models for brushstroke simulation and painting robot control. One of the main features of the brush is its compliance, which describes the relationship between the brush footprint shape and the pressure applied to the brush. In addition, during motion, the brush footprint position lags from the brush handle position in a complicated manner. To date, the question of creating a physically correct model of these effects and choosing the best method for the model parameter calibration has not been presented in the literature. In the current paper, we derive equations of the brush contact patch motion, give their closed-form solutions, and investigate three methods for the brush model calibration: capturing brush footprints on a matte glass with a camera, painting calibration brushstrokes, and capturing a brush shape side projection with a camera. As we show, calibration brushstrokes give us primary information on brush contact patch displacement during painting, and capturing the brush side projection allows the accurate estimation of the gap from the brush tip to the center of the contact patch. Capturing brush footprints is useful for creating a brushstroke executable model. As an example, a model for a round artistic brush was created and verified in three tests, including measuring the coordinates of an angular brushstroke center line, simulating an angular brushstroke, and writing a signature using a robotic setup.

Emulating Artistic Expressions in Robot Painting: A Stroke-Based Approach

Representing art using a robotic system is part of artificial intelligence in our lives, especially in the realm of emotional expression. Developing a painting robot involves addressing how to enable the robot to emulate human artistic processes, which often include imprecise techniques or errors akin to those made by human artists. This paper discusses our development of an innovative painting robot utilizing the sim-to-real approach within learning technology. Specifically, this pipeline operates under a deep reinforcement learning (DRL) framework designed to learn drawing strategies from training data derived from real-world settings, aiming for the robot’s proficiency in emulating human artistic expressions. Accordingly, the framework comprises two modules when given a target drawing image: the first module trains in a simulated environment to break down the target image into individual strokes; the second module then learns how to execute these strokes in a real environment. Our experiments have shown that this system can meet our objectives effectively.

Control of a painting robot containing parallelogram linkages based on its distinctive dynamic characteristics

To achieve high-performance control, the dynamics and control should be closely integrated. This paper focuses on dynamic-based control design for a hybrid painting robot containing parallelogram linkages to improve its tracking accuracy and motion consistency. The study derives the dynamics of the parallelogram component to investigate its distinctive attributes, which include time invariance, joint independence, and linear approximability. Based on these unique characteristics, a novel theory-experiment mixed two-step method for control design is presented. It involves identifying and visualizing an optimized space of control parameters through theoretical deduction, followed by determining the optimal control parameters within this space through experimental trials. Experimental results underscore a substantial improvement in the tracking accuracy and motion consistency of the painting robot.

Painting robot with three axis motion

Paint was applied directly to the painted surface while painting with a brush. There are occasionally unsatisfactory outcomes, such as paint stacking or streaks remaining. Spray guns yield better paint results, therefore a robot was developed that can regulate the spray gun's movement in order to apply paint. A painting robot that resembles an arm is one that has three axes of movement. The robot can move left-right on the x axis, up-down on the y axis, and back-front on the z axis. Its end effector is a spray cannon. An ultrasonic sensor is used to measure the distance between components and the spray gun; when the sensor measures near distances, such as between 4 and 8 cm, the test sensor distance measurements have a larger average error. When the test reading distance is increased from 10 cm to 14 cm, the average error reduces. Superior painting outcomes can be achieved with a paint distance of 10 cm, an air pressure of 80 psi, and a spray gun arm movement speed of 25 rpm. With average x-axis movement of 74.6 cm, y-axis movement of 14.8 cm, and z-axis movement of 88.2 cm, the largest volume that can be painted is 97,517.5 cm3. The rotation of the arm is used to determine the angular rotation needed to paint components. Painting the left side of the component at an angle of 10°, the back side at an angle of 100°, the right side at an angle of 190°, and only painting the front side of the component at the required angle of 90° require the rotational movement of the arm to the left.

A Review Paper on Paint Spraying Robot

Abstract: Painting interior walls is a frequent construction task that takes a lot of time and work. Robotic painting was introduced to replace human manual activity, improving accuracy, efficiency, and lowering costs. In this study, We present an independent robot that paints walls. robot that uses a cascade lift mechanism to enable it to use a paint sprayer to paint a room's interior walls. The paint sprayer can reach the necessary heights With the assistance of of this mechanism for cascading lifts mechanism. With two degrees of freedom (DOF), The robot moves fluidly in each of the six directions. thanks to the DC powered mecanum wheels mounted down to its foundation. Ultrasonic sensors are used by the robot to measure distance, make adjustments to the walls, and determine if the mister has reached the wall's summit. The robot's mecanum wheels, ultrasonic sensors, and other components are all managed by the master controller. The AC power supply powers the entire system Color is sprayed on to manage the amount of color; sprayers are employed for this purpose. Sprayers need to split liquid into droplets that are the right size, distribute them evenly across the surface, and manage the volume of liquid to prevent overapplication. One major issue thataffects workers everywhere is disease control

Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning

Optimizing IRB1410 industrial robot painting processes through Taguchi method and fuzzy logic integration with machine learning

Design and Application of AI-based Brush Calligraphy and Painting Robot

Design and Application of AI-based Brush Calligraphy and Painting Robot

A new algorithm for obstacle avoidance and tracing applied to wall painting robots

A new algorithm for obstacle avoidance and tracing applied to wall painting robots

Smart monitoring and automated real-time visual inspection of a sealant applications (SMART-VIStA)

Computer Vision (CV) assisted robotics applications are receiving significant attention with increased automation on the factory floor. The automated solutions provide a pathway to smart manufacturing and improve the overall product quality inspection process. We propose a closed-loop framework – Smart Monitoring and Automated Real-Time Visual Inspection of Sealant Application (SMART-VIStA) to address critical challenges in process automation and optimized process parameters feedback. This novel modular approach combines CV-based robotic path planning, deep learning-based classification, image segmentation, and real-time recommendations for corrective actions in a sealant deposition process. Specifically, the system includes pose detection and localization for the rectangular deposition plate, predicting and segmenting the glue dot class through few-shot learning, quantifying the quality of the glue dot through the unique shape quality index for circular artifacts, and an advisory recommendation system through Bayesian Decision Network. Monitoring of real-time results through a graphical user interface (GUI) allows the flexibility to change the parameters for subsequent cycles. A prototype for this process has been set up at the Boeing manufacturing facility to demonstrate its effectiveness. With accuracies extending beyond 90% in multiple tasks, this framework has promising applications in sealant inspection and can extend to different manufacturing scenarios, such as robotic welding and robotic painting.

Development of Paint Thickness Simulator for Industrial Painting Robots

Development of Paint Thickness Simulator for Industrial Painting Robots

Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching

The current success of Reinforcement Learning algorithms for its performance in complex environments has inspired many recent theoretical approaches to cognitive science. Artistic environments are studied within the cognitive science community as rich, natural, multi-sensory, multi-cultural environments. In this work, we propose the introduction of Reinforcement Learning for improving the control of artistic robot applications. Deep Q-learning Neural Networks (DQN) is one of the most successful algorithms for the implementation of Reinforcement Learning in robotics. DQN methods generate complex control policies for the execution of complex robot applications in a wide set of environments. Current art painting robot applications use simple control laws that limits the adaptability of the frameworks to a set of simple environments. In this work, the introduction of DQN within an art painting robot application is proposed. The goal is to study how the introduction of a complex control policy impacts the performance of a basic art painting robot application. The main expected contribution of this work is to serve as a first baseline for future works introducing DQN methods for complex art painting robot frameworks. Experiments consist of real world executions of human drawn sketches using the DQN generated policy and TEO, the humanoid robot. Results are compared in terms of similarity and obtained reward with respect to the reference inputs.

Robotic Writing of Arbitrary Unicode Characters Using Paintbrushes

Human handwriting is an everyday task performed regularly by most people. In the domain of robotic painting, multiple calligraphy machines exist which were built to replicate some aspects of human artistic writing; however, most projects are limited to a specific style of handwriting, often Chinese calligraphy. We propose a two-stage pipeline that allows industrial robots to write text in arbitrary typefaces and scripts using paintbrushes. In the first stage, we extract a set of strokes from character glyphs which are similar to how humans choose strokes during writing. In the second stage, we generate corresponding brush trajectories by applying a brush model to the extracted strokes. Our brush model computes the required brush pressure to achieve the given stroke width while also accounting for brush lag. We also present a method to automatically measure the parameters needed to predict brush lag by painting and recording calibration patterns. Our method generates trajectories for text in any given typeface, which, when executed by a robotic arm, results in legible written text. We can render most writing systems, excluding emoji and ligatures, in which arbitrary texts can be specified to write.

Robotic painting: mimicking human applicators

Robotically assisted painting is widely used for spray and dip applications. However, use of robots for coating substrates using a roller applicator has not been systematically investigated. We showed for the first time, a generic robot arm-supported approach to painting engineering substrates using a roller with a constant force at an accurate joint step, while retaining compliance and thus safety. We optimized the robot design such that it is able to coat the substrate using a roller with a performance equivalent to that of a human applicator. To achieve this, we optimized the force, frequency of adjustment, and position control parameters of robotic design. A framework for autonomous coating is available at https://github.com/duyayun/Vision-and-force-control-automonous-painting-with-rollers; users are only required to provide the boundary coordinates of surfaces to be coated. We found that robotically- and human-painted panels showed similar trends in dry film thickness, coating hardness, flexibility, impact resistance, and microscopic properties. Color profile analysis of the coated panels showed non-significant difference in color scheme and is acceptable for architectural paints. Overall, this work shows the potential of robot-assisted coating strategy using roller applicator. This could be a viable option for hazardous area coating, high-altitude architectural paints, germs sanitization, and accelerated household applications.

A Robot for Artistic Painting in Authentic Colors

A Robot for Artistic Painting in Authentic Colors

Automated Wall Painting Robot for Mixing Colors based on Mobile Application

Automated Wall Painting Robot for Mixing Colors based on Mobile Application

Design and Implementation of Painting and Replacement Automation Robot System in Spindle Line for Smart Manufacturing

In the process of painting the surface of injection products, the spray trajectory and speed are very important and have a very important effect on productivity and quality. However, since it is very difficult to standardize and quantify each product, it was difficult to apply automation to the painting process, so there were many cases where people directly operated it. Recently, with the development of robot technology, many manufacturing processes performed by humans are being replaced. To automate painting in the spindle line of the painting process, we design Painting and Replacement Automation Robot System in Spindle Line. By implementing it in an actual production line, we have greatly improved productivity and reduced paint replacement time.

Design and Development of Elevation Painting Robot

Design and Development of Elevation Painting Robot

Guidance the Wall Painting Robot Based on a Vision System

Guidance the Wall Painting Robot Based on a Vision System