Robotics has always fascinated the human imagination, from industrial automation to autonomous vehicles and, recently, even humanoid robots like Tesla's Optimus. But behind these robots' sleek, futuristic exterior lie decades of development in three critical areas: robotics, vision, and control. In this blog post, we'll dive into the fascinating world of robotics, drawing on insights from the leading experts Peter Corke from the Queensland University of Technology and Witek Achimczyk and Remo Pillat from MathWorks. 

The Foundations of Robotics: Perception, Planning, and Action

At its core, a robot must be able to perceive its surroundings, plan a course of action, and then execute that plan - three fundamental stages of robotics development. Let’s explore these stages in detail, providing valuable insights into how robots function in structured environments such as factories and more complex, dynamic environments like construction sites and forests.

The first step is perception, which involves using sensors and cameras to gather information about the world. Whether it’s LIDAR (Light Detection and Ranging), cameras, or IMUs (Inertial Measurement Units), modern robots use a variety of sensors to understand their environment. Advanced perception systems allow robots to develop 3D models of their surroundings and identify objects.

The Computer Vision Toolbox from MathWorks is one example of how engineers and researchers use advanced algorithms to interpret camera visual data in real-time, transforming raw images into actionable insights.

The second step is planning. Once a robot perceives its environment, it must decide how to navigate it. This is where motion planning algorithms come into play. Planning involves deciding the best path from point A to point B while avoiding obstacles and optimizing the robot's movements. While this area has evolved significantly, there is still room for AI-driven improvements in planning algorithms.

Finally, the robot must act on its plans. Depending on the robot's form, this involves moving limbs, wheels, or propellers. The Robotics System Toolbox from MathWorks simplifies the development of control systems, making it easier for engineers to program robots to move efficiently and safely.

The Rise of Modern Robots: From Early Machines to Humanoids and Autonomous Systems

The history of robotics dates back to the 1950s, with the development of the first modern industrial robot, Unimate. Developed by Unimation in Connecticut, Unimate's job was to remove hot metal pieces from a die-casting machine—a dangerous task for human workers. This was the first generation of robots designed to work in structured environments like factories, where their tasks were clearly defined and their surroundings were predictable.

These first-generation robots were rudimentary in their perception, lacking vision and sophisticated sensors. Peter Corke mentions that these robots were extremely "dumb," but highly effective at repetitive tasks like assembling cars or packaging products. However, the real challenge for roboticists today lies in building robots that adapt to dynamic, unstructured environments, such as homes, forests, or urban spaces.

Fast-forward to today, and robotics has entered a new phase, often called Industry 4.0, in which robots are becoming increasingly sophisticated. Modern robots are being deployed in warehouses, autonomous vehicles, and even for complex tasks such as surgery. Companies like Tesla are pushing the boundaries with their humanoid robot, Optimus, which is designed to assist with physical tasks in unstructured environments like homes or construction sites.

However, one debate in the robotics community remains: Do robots need to look like humans? While humanoid robots like Optimus attract attention, many roboticists argue that robots designed for specific tasks (e.g., vacuuming, and picking objects in warehouses) don’t need to mimic human form to be effective. The complexity of building humanoid robots adds unnecessary engineering challenges, including higher costs and maintenance, compared to simpler, more specialised machines.

Still, humanoids hold promise. Humanoid robots can operate in environments built for humans, using existing tools and spaces without Artificial intelligence (AI), especially in deep learning and reinforcement learning, which is a critical component of advancing modern robotics in the entire world to accommodate robots. This vision for humanoid robots was previously considered decades away, but recent advancements suggest we may see practical applications sooner than expected.

The Role of AI and Deep Learning in Robotics

Artificial intelligence (AI), especially in the areas of deep learning and reinforcement learning, is a critical component of advancing modern robotics. In the past, solving perception problems required highly specialised programming tailored to specific environments. Today, deep learning algorithms can recognize objects, interpret sensor data, and even learn from experience, making them far more versatile.

MathWorks provides essential tools for integrating AI into robotic systems. The Lidar Toolbox and the Image Processing Toolbox are particularly useful for interpreting 3D sensor data and images, which robots use to navigate and interact with the physical world. For those building or optimizing AI-driven robots, MathWorks also offers a comprehensive list of all its toolboxes, including those for deep learning, reinforcement learning, and control systems.

The Growing Industry Demand for Robotics

The demand for robotics in the manufacturing, healthcare, and logistics industries is skyrocketing. Robots are being used to automate repetitive tasks, reduce human error, and improve safety in environments like factories and warehouses. 

MathWorks plays a crucial role in this transformation. The Robotics and Autonomous Systems solutions provide engineers and developers with the tools to design, simulate, and deploy robotic systems across various industries. These tools, from autonomous vehicles to medical robots, support every development step, from initial modeling to deployment. 

Education and Open Source in Robotics

Programming and mathematics are essential skills for those looking to get started with robotics. A strong foundation in these areas is necessary to build and understand robotic systems. Tools like MATLAB offer an accessible platform for beginners and students to start experimenting with robotic projects.

If you’re interested in diving deeper into the subject, Peter Corke has also authored a popular book, Robotics, Vision and Control: Fundamental Algorithms in MATLAB, now in its third edition. It’s a great resource for anyone interested in learning about the technical aspects of robotics, from perception to control. You can find the book at Springer or Amazon.

For hands-on learners, Peter also provides open-source code through his RVC3-MATLAB repository on GitHub. This repository includes all the code and examples needed to start building your own robotics systems!

Conclusion: The Expanding Role of Robotics in Modern Society

The field of robotics is proliferating, driven by advancements in AI, deep learning, and autonomous systems, leading to remarkable innovations. Robots are no longer confined to industrial environments; they are entering homes, hospitals, and even autonomous vehicles. With powerful tools like MathWorks, researchers, and engineers can now design and deploy advanced robotic systems more efficiently than ever before.

As you explore the exciting world of robotics, remember that today's tools and resources - like the Robotics System Toolbox and various AI and perception toolboxes - make it easier to get started or advance your knowledge. The future of robotics is bright, and as Peter, Witek, and Remo have shown, the possibilities are numerous. So whether you're building your first robot or looking to develop the next breakthrough in AI-driven systems, the tools and knowledge are at your fingertips!