Capturing coordinates - click for full image

Products everywhere have gotten so small that many necessitate automated systems specifically designed to feed, align, and fasten small and complex 3D parts. A key tool in these systems is vision guidance — using machine vision to locate parts to be accessed by a robot. Consumer demand for digital and cell phone camera technology has greatly reduced the cost of high-resolution industrial cameras, so they can be economically integrated into automated systems. However, these cameras can be put to use in different ways.

Traditional vision guidance is an excellent way to find parts, but its functionality in small, high-precision assembly operations is limited by the accuracy of the robot it controls. For example, placing a 1 mm² laser diode or a semiconductor disk-drive read head requires accuracy down to two or one microns. For placement accuracies in this range, traditional machine vision requires extremely expensive, high-accuracy robots. What's more, the ability to move to an absolute XYZ position commanded by traditional vision is limited by the robot's manufacturing variations, thermal expansion, and a host of mechanical effects.

With visual servoing, which closes the robot's position loop using visual feedback, robot placement accuracies are based on encoder resolution rather than absolute accuracy. So, applications such as placing laser diodes into DVD read heads or teaching wafer slot positions in a semiconductor wafer carrier can be effectively automated using low-cost robots with limited intrinsic absolute accuracy. The only requirement is good robot move resolution.

Threading the needle

In a traditional vision guidance application, the vision system captures a single image and locates a target's position in world coordinates. The system then transmits these coordinates to a robot, and relies on the mechanism to accurately move to the target position. This is analogous to a person looking at a part, and then grabbing it with their eyes closed: If the part is big enough, this method may be successful. However, for finer operations, this approach is not effective.

So how else can machine vision operate? Just as a human performs finer tasks, such as threading a needle: By looking at both objects simultaneously, determining relative distance and direction, and moving the thread toward the eye of the needle accordingly. In fact, this is how robotic visual servoing works. The vision system takes a picture and analyzes the relative positions of actuator and target. Instead of sending the robot a single motion command in world coordinates, the software sends a series of incremental distance and direction motion commands. As the motion is executed, more pictures are taken. The software continuously analyses the new images and updates the motion commands accordingly — until the vision system confirms that the task is accomplished.

Resolution, not accuracy

A series of incremental motion commands - click for full image

In a traditional vision guidance system, it's the robot's responsibility to move to the commanded location accurately and in a repeatable manner. The success of this process relies on many factors:

• An accurate knowledge of the camera's position relative to the robot
• An accurate knowledge of where the camera is looking (field of view)
• The robot's ability to accurately move to a commanded XYZ position after taking into account the effects of thermal expansion, runout, backlash, drive train wear, and manufacturing tolerances in the straightness and perpendicularity of its links

Because there's no verification that the motion is executed correctly, the system must assume the process is successful and progress to the next step. However, even the most accurate mechanisms require periodic recalibration of the entire system. Visual servoing replaces a single world coordinate command in favor of a series of distance and direction commands, so the need for absolute accuracy is significantly reduced.