-- This paper proposes a novel control framework for robotic swarms capable of turning a musical input into a painting. The approach connects the two artistic domains, music and painting, leveraging their respective connections to fundamental emotions. The robotic units of the swarm are controlled in a coordinated fashion using a heterogeneous coverage policy to control the motion of the robots which continuously release traces of color in the environment. The results of extensive simulations performed starting from different musical inputs and with different color equipments are reported. Finally, the proposed framework has been implemented on real robots equipped with LED lights and capable of light-painting.

High demand for painters is required nowadays and foreseen in the near future for both developed and developing countries. To satisfy such demand, this paper presents the detailed computer aided design (CAD) model of a fully functional wall painting robot for interior finishes. The RoboPainter is capable of performing full scale wall-ceil painting in addition to decorative wall drawings. The 8 degrees of freedom (DOF) mobile robot structure consists of a 6DOF spray painting arm mounted on a 2DOF differentially driven mobile base. The design presented endows several achievements in terms of total robot mass and painting rate as compared to existing literature. Detailed dynamic model parameters are presented to allow for further enhancement in terms of robot motion control.

In the world of AI, two remarkable technologies have made major strides over the last 18 months. One is called Large Language Models (LLMs), which enables AI systems to generate convincing essays and hold convincing conversations, and even write successful computer code, all with little or no human supervision. The second is called Generative Art, which enables automated software to create unique images and artwork from simple text prompts. Both are rooted in similar AI technology and have wowed the world with extremely impressive results. The headline image at the top of this article is one that I created using an AI-based generative artwork system.

Artificial intelligence is advancing at an incredible pace. If you haven't been following their progress, you'll be surprised to learn about AI's widespread adoption in every facet of life. A massive AI revolution is happening right now in the business world. According to the World Economic Forum, 85 million jobs will disappear, and 97 million new ones will emerge thanks to AI by 2025. We've entered an era where machines are no longer mere tools in the hands of humans but companions that work side by side and provide enormous value to any industry.

The arrival of the robotic arm was not a happy affair at Professional Finishing in Richmond, California, just across the bay from San Francisco. In contrast to the hulking factory arms that have traditionally labored in isolation, this robot was meant to work right alongside humans, delicately sanding and painting things like speaker cases or cabinets for medical devices. Which sounded a lot like a first step toward replacing the company's workers altogether. "We did have one employee tell us, 'Hey let me know when the robot's up and running and I'll just quit,'" says Professional Finishing co-owner Dawn White. "We said, just bear with us. Help us and everybody will keep their jobs."

Deploying Constraint Programming for Testing ABB’s Painting Robots

Moreover, coming up with possible failure scenarios that may arise during system operation and determining triggers for these cases is a major challenge. ABB Robotics' painting robots are advanced distributed systems that must be thoroughly tested before they are shipped to customers, mostly from the automotive and avionics domains. Improving the overall quality of painting robots has always been of utmost importance because it is viewed as a competitive advantage in the market for industrial robots -- an error in the control system of these robots, uncovered in the field, can have serious economic consequences. After some tuning, the model was able to occur when the robot's speed increases and switching generate test scenarios that could find all the re-introduced between different paint spray patterns occurs too bugs. This was considered a significant success rapidly.