What new robotics skills do you need to learn?

With skills, it is also important to be aware of what resources are already existing so you spend the time in innovating rather than reinventing. It is important to understand what pre-built libraries are available in software packages such as OpenCV, ROS libraries such as NAV2 or MATLAB Toolboxes such as Navigation Toolbox and Robotics System Toolbox. Furthermore, many of these software tools interface with each other so you can use the functionalities where you find them best. Cloud simulation and platforms for training AI algorithms are also important to learn.

Proficiency in robotics necessitates adeptness in software development. You must be adept at writing code for controlling the robot's behavior, perception, and communication. Different programming languages, frameworks, and tools like Python, C++, ROS, MATLAB, and OpenCV may be necessary, depending on the robot's type and task. Moreover, familiarity with software engineering principles, including version control, testing, debugging, and documentation, is crucial for efficient and effective robotics software development.

For a solid foundational understanding, you are going to want Python within your skillset as this covers most robotic control. If you want to get more in depth with robotics control/simulations, then ROS, C++ and MATLAB are musts. Finally, RoboDK is a very good place to start to robot programming and simulation.

What I have found to be the best way to learn essential hardware as well as software skills is by embarking on a robotics project that challenges you in the concepts you are interested in learning. Make sure to choose a project that not only teaches you but also excites and motivates you. The joy of seeing your efforts culminate in a functional robot is a powerful driving force. Moreover, this kind of project serves as a testament to your capabilities, showcasing your accomplishments. It not only deepens your understanding of robotics by learning using a hands-on approach but also pushes your creativity and problem solving skills to the limit. Such projects are invaluable for personal and professional growth in the world of robotics.

For most tasks It's important to ask the question Are classical methods enough or not.Often times in the hype of it all we tend to overengineer and thus Machine Learning methods could be an overkill. The idea is for both classical and Machine Learning methods to co-exist such that both reliability and robustness is maximised for a selected task.

I couldn't agree more with the importance of teamwork and coordination in robotics. From defining project scopes to managing resources and risks, effective collaboration is key to success.

There are tons of amazing opportunities to learn about the latest in robotics by either new technology, capabilities or standards! A3, IEEE, ASTM are all great resources. I'd also highly recommend figuring out how you can go to local training sessions to see how EOAT, Vision and add-ons can be used to advance your robotic knowledge.

Continuous learning in robotics means more than being up to date with the latest libraries or hardware. It is also about keeping up with the latest research, innovations, and best practices, all while having the vision to reimagine what robots can accomplish. The great thing about robotics field is that it fuses technical know-how with creativity. Breakthroughs in the field of robotics happen when we step outside the confines of traditional thinking and dare to imagine what was previously considered impossible. True roboticists excel not only in technical skills but also in seeing everyday challenges as opportunities for innovation.

Robot choreography/ co-operation in unstructured dynamic environments is an upcoming area to focus on. Here's why: - Enhanced Efficiency: Collaborative robots swiftly adapt, share tasks, and optimize their actions, minimizing downtime - Improved Safety: Cooperation ensures secure movement, reducing risk for both humans and equipment - Scalability: With increased task demand, more robots can be seamlessly integrated into swarms, providing flexibility to handle various scenarios without the need for significant reprogramming - Resource Optimization: Data sharing conserves energy and extends robot lifespans - Technological Advancement: Robot choreography drives autonomous system innovation, paving way for future breakthroughs