Robotic tool changers (WPDs) make robot applications more flexible by enabling robots to automatically change different end effectors or peripheral devices. These end effectors and peripheral devices include, for example, spot welding torches, grippers, vacuum tools, pneumatic and electric motors. A WPD includes a robot side for mounting on the robot arm and a tool side for mounting on the end effector. WPDs allow different media, such as gases, electrical signals, liquids, video, and ultrasound, to be connected from the robot arm to the end effector. The advantages of robotic tool changers include:
1. Production line changeover can be completed within seconds;
2. Maintenance and repair tools can be quickly replaced, greatly reducing downtime;
3. Increase flexibility by using more than one end effector in the application;
4. Replace the original bulky and complex multi-functional tooling actuator with an automatically changing single-function end effector.
Quick-change robotic tools enable individual robots to interchange different end effectors during manufacturing and assembly processes, increasing flexibility. They are widely used in automated spot welding, arc welding, material handling, stamping, inspection, crimping, assembly, material removal, deburring, and packaging. Furthermore, in critical applications, quick-change tools provide backup tools, effectively preventing accidents. Compared to manual tool changes that can take hours, quick-change tools can automatically replace tools in seconds. Simultaneously, this device is also widely used in non-robotic fields, including platform systems, flexible fixtures, manual spot welding, and manual material handling.
Industrial robot vision guidance and positioning
For industrial robots operating on automated production lines, the most common type of operation they perform is the "grasp-place" action. To complete this type of operation, it is necessary to acquire the positioning information of the object being manipulated. First, the robot must know the object's pose before being manipulated to ensure that the robot can grasp it accurately; second, it must know the object's target pose after being manipulated to ensure that the robot can complete the task accurately.
In most industrial robot applications, robots operate according to fixed programs. The initial and final poses of objects are predetermined, and the quality of task completion is guaranteed by the positioning accuracy of the production line. High-quality operation requires a relatively fixed production line with high positioning accuracy. This results in decreased production flexibility but significantly increased costs, creating a conflict between production line flexibility and product quality.
Visual guidance and positioning are ideal tools for resolving the above contradictions.
Industrial robots can use vision systems to understand changes in the working environment in real time and adjust their actions accordingly to ensure the correct completion of tasks. In this case, even if there are large errors in the adjustment or positioning of the production line, it will not have a significant impact on the robot's accurate operation. The vision system actually provides an external closed-loop control mechanism to ensure that the robot automatically compensates for errors caused by environmental changes.
Ideally, visual guidance and positioning should be based on visual servoing. First, the approximate location of the object is observed. Then, the robotic arm moves while simultaneously observing the deviation between itself and the object. Based on this deviation, the robotic arm's movement direction is adjusted until the robotic arm and object make precise contact. However, this positioning method presents numerous challenges in its implementation.
Direct vision guidance and localization involves providing a detailed description of the spatial pose of an object in the robotic environment in a single step, guiding the robot to directly perform actions. Compared to vision servoing-based methods, direct vision guidance significantly reduces computational load, creating conditions for practical applications. However, this relies on one prerequisite: the vision system must be able to accurately determine the three-dimensional pose information of the object in robot space (base coordinate system).
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