Description: Visual Servoing is a control technique that uses visual feedback to guide the movement of a robot. This methodology is based on the ability of robotic systems to interpret images and visual data in real-time, allowing the robot to adjust its behavior and trajectory based on what it ‘sees’. Through computer vision algorithms, the robot can identify objects, recognize patterns, and calculate distances, enabling it to interact more effectively with its environment. Key features of visual servoing include the ability to adapt to changes in the environment, improved movement accuracy, and the capability to perform complex tasks that require high coordination. This technique is particularly relevant in applications where precision and adaptability are crucial, such as in object manipulation, autonomous navigation, and human-robot interaction. In summary, visual servoing represents a significant advancement in robotics, integrating computer vision with motion control to create smarter and more autonomous systems.
History: The concept of visual servoing began to develop in the 1980s when researchers started exploring the integration of computer vision into robotic systems. One important milestone was the work of Hiroshi Ishiguro and his team at Osaka University, who demonstrated the feasibility of using real-time images to control robots. Over the years, the technique has evolved with advancements in vision algorithms and increased computing power, enabling more complex and precise applications.
Uses: Visual servoing is used in various applications, such as industrial robotics for object manipulation, in autonomous vehicles for navigation and obstacle avoidance, and in robotic assistance systems for human interaction. It is also applied in medical robotics, where precision is crucial, and in unmanned aerial vehicles for target tracking.
Examples: An example of visual servoing is the Baxter robot, which uses cameras to identify and manipulate objects on an assembly line. Another case is the use of drones equipped with cameras that can follow a moving target, adjusting their trajectory in real-time. Additionally, in medical robotics, systems like the Da Vinci Surgical System use visual servoing to perform procedures with high precision.
