Seu setor está mudando rapidamente. Quais são as melhores ferramentas de automação robótica para acompanhar?

To keep up with the rapidly evolving robotics landscape, incorporating Robotic Process Automation (RPA) tools is key. RPA, a software technology, emulates human actions to automate routine, rule-based tasks like data entry, invoicing, and customer service. This not only minimizes errors but also enhances efficiency and customer satisfaction. RPA tools often feature a graphical user interface (GUI) for designing and executing workflows and seamlessly integrate with diverse applications and systems. Good examples of RPA tools include UiPath, Automation Anywhere, and Blue Prism.

Enter the world of robots with tools like RPA & collaborative robots – it's where the future gets exciting. Robotic Process Automation (RPA) is like computer software that copies what people do, making it do repetitive tasks like entering data or helping customers. RPA helps to avoid mistakes, save time & make customers happier. RPA tools typically use GUI to design & execute workflows, & can integrate with various applications & systems. RPA tools usually have an easy design & use pictures to set up and do tasks. They can work with different programs & systems. Some examples of RPA tools include UiPath, Automation Anywhere & Blue Prism. Dive into robotics, keep up with new tech, & shape future where innovation & simplicity go hand in hand.

I think Opensource Play a vital role here, with this tool on : 1.Robot Operating System (ROS): This is a brain for your robots. ROS helps them communicate, move, and perform tasks efficiently. 2.OpenCV : This tool is like the eyes of your robots. It enables them to see and understand the visual world, crucial for tasks like object recognition. 3.TensorFlow and PyTorch: Learning tools for your robots. They allow your robots to adapt and learn from their experiences, making them smarter over time. 4. Gazebo: Test your robotic systems in a computer environment before deploying them in the real world. 5. Robot Framework: This is coordinator for your robots. It helps in designing, executing, and managing robotic automation tests.

RPA is a technology that automates repetitive tasks traditionally done by humans, like data entry and customer service. It's efficient and reduces errors, thereby saving time and enhancing customer satisfaction. RPA uses a graphical user interface for creating workflows and can work with different systems and applications. Popular RPA tools include UiPath, Automation Anywhere, and Blue Prism. This technology streamlines processes across various industries, making operations more efficient.

I would pursue an advanced degree in robotics, engineering, or a related field, focusing on gaining in-depth knowledge and specialized skills. I would also actively engage in research projects, publications, and collaborations to build practical experience and establish a strong presence in the academic and professional robotics community.

Being in this Robotics field for almost a decade! I feel, ROS is an indispensable asset in the field of robotics. Its adaptability, emphasis on communication and collaboration, robust testing tools, and community-driven development make it a pivotal framework for advancing robotic software. As robotics continues to evolve and permeate various industries, ROS remains at the forefront, contributing significantly to the growth and sophistication of robotic applications worldwide.

In the rapidly evolving landscape of robotics, one of the key tools to stay ahead is the Robotic Operating System (ROS). As an open-source framework, ROS offers versatile libraries and tools for developing and executing robotic software. Its capabilities span different robot types, fostering seamless communication, coordination, and control. ROS also facilitates simulation, testing, and debugging of robotic applications. The suite includes ROS Core, establishing fundamental infrastructure, ROS Desktop with graphical tools and visualization, and ROS Industrial, specializing in industrial robotics. Embracing ROS empowers professionals to navigate and innovate in the dynamic field of robotics.

ROS being a middleware, will only help communicate between different codes (nodes) that run parallely. Alongside effecient architecture and optimized codes, ROS can be used to setup a flexible platform for collaborative development. Some things to keep in mind to have clean dec phase: 1. Have seperate packages for all publishers, subscribers, msgs, and services (unless you need a node to do talk and listen). 2. Follow namespacing strictly, even for naming packages. 3. Leverage existing packages. There are lot of people working with ROS, so it's better to not reinvent the wheel. 4. Have a clean and documented code. Have intutive naming for variables. 5. When publishing, it's highly recommended to pubish it with launch files.

ROS also has limitations, such as its complexity and resource demands. Consider exploring these alternatives alongside ROS: ROS 2: The next iteration of ROS addresses scalability and performance concerns, making it suitable for complex robots and real-world deployments. ROSjects: This framework uses JavaScript for robot software, offering a more web-friendly approach compared to C++-based ROS. MicroPython for robotics:

A nice idea is to have a predefined setup with ROS. it must be written in a very modular way, and very loosely coupled. So it enables you to replace each part with a new software or method that you find and you always can keep up with new changes and technologies.

MuJoCo and Isaac Sim each bring unique strengths to robotic simulation. MuJoCo excels in accurately simulating complex physical interactions, crucial for projects involving precise robotic movements. For instance, it significantly aided in a project requiring detailed manipulation tasks. Isaac Sim, powered by NVIDIA, offers realistic environment simulations, making it ideal for training AI algorithms in varied, real-world-like scenarios. Its use in an autonomous navigation project provided diverse conditions for effective AI training. The choice between these tools depends on project needs, such as the level of physical detail or environmental realism required, showcasing their distinct contributions to robotics

Sim Development: In my opinion, simulation is tightly coupled with the product end use and the amount of factors you'd want to consider during deployment. If you intend for the user to select the specs based off a sim run, then you should provide enough leeway to customize the product in an intutive way. Sim Test: In general simulation is a place you can run wild doing absurd stuff to see how the results might turn out. So don't be shy to try out un-natural stuff too.

In the industrial field, one of the most used brands is iRVision, this is the FANUC brand vision system, and it is easy to retrain if a part that interacts with the robot change. You can use this type of system to find different parts in a 2D or 3D environment, the robot will move to a specific position, the camera will take a photo and, using mainly contrast, find the pattern that was previously trained. the system will calculate the displacements and the robot will move to the previously trained picking position, but it will add the displacements that the vision detects, and if the part changes it will only need to retrain the vision and the positions with which the robot interacts with the part.

Vision systems have evolved with AI assistance, expanding their roles beyond providing supplemental information to robots. They now play crucial roles in tasks such as robot path planning, collision avoidance, and replacing encoders through visual tracking.

To keep up with the rapidly changing robotics and automation industry, tools like Robotic Process Automation (RPA) platforms such as UiPath, Automation Anywhere, and Blue Prism are essential. Additionally, ROS (Robot Operating System) for robotics development, and simulation software like Gazebo for testing and development, are highly regarded. For more specialized applications, TensorFlow and PyTorch offer powerful machine learning libraries to integrate AI capabilities into robotic systems.

Fortunately the official documentation of software in Robotics are very great. ROS has a very great website with lots of tutorials and guides. Other tools in Robotics are also the same: PyTorch, Gazebo, Carla, among others. Just try to do whatever is in the docs step by step.

Collaborative robots simplify the integration of a robot into a conventional production line, eliminating the need for complicated safety measures. These robots can be mounted on mobile AGVs, allowing easy relocation to different stations.

If you want to stay up to date on how robotics is changing, you go to conferences and tradeshows around those events. Two especially come to mind: The Robotics Summit and Expo in Boston, MA, focuses on the Design, Development, and Commercialization of Robotics Systems. Automate in Chicago, IL. This focuses on the the integration and technology around collaborate, industrial and cartesian robotic systems that are deployed in a wide variety of manufacturing industries and warehouse/logisitic, etc.

I just want to add that some of the vision tools are free, and you can download them from the Internet, you can try the OpenCV. Each of the brands has some pros and cons.

In rapidly changing industry, keeping up with the best robotic automation tools is crucial. Apart from well-known options like RPA, ROS, robotic simulation, vision, learning, & hardware tools, consider incorporating various technologies. This includes using collaborative robots, machine learning, & computer vision for advanced automation. Integration of sensors, 3D printing, & swarm robotics can enhance efficiency. Swarm robotics is when a bunch of small robots work together as a team to perform tasks, just like group of animals collaborating in nature. Explore emerging fields like augmented reality, cloud robotics, & edge computing. Incorporating autonomous mobile robots, soft robotics, & predictive maintenance tools ensures adaptability

1. Focus on versatility and adaptability: Look for tools that can handle a wide range of tasks across different platforms and applications. This avoids getting locked into specific technologies and gives you more flexibility as your needs change. 2. Prioritize ease of use and low code/no code options: Especially if you're not a programming expert, these tools can empower you to build and deploy automations without extensive coding knowledge. This can accelerate your learning and implementation process.