Adding vision to a collaborative robot can open a world of possibilities for automation applications. With a vision system, a robot can inspect parts, check specific features of a part, recognize a part to pick it up, count items, adjust its path using visual feedback, color sort, and so on. The breadth of applications requires careful consideration to ensure selection of the right technology for the job.
Two common applications for collaborative robots are pick-and-place systems and quality control. Upfront evaluation will help determine if these systems would benefit most from a vision system or an alternative.
Pick and Place
The feature in a pick-and-place application that most influences the decision between using a vision system or another option is the part presentation. The robot must know where the parts are to pick them up. Following are some options that will solve part localization for pick-and-place applications.
Note that this discussion is about the pick position, but the same factors apply to the place position. Depending on the system requirements when placing a part, a vision system may be needed to determine the proper place position. An example of this might be locating the box in which the part is to be placed.
Parts can be placed in trays or secured with fixtures, so they are always at a constant position relative to the robot. This is usually simple to implement and program because the robot only needs to learn those stationary positions. Some robot models come with palletizing programs that will help a user teach positions quickly. If this kind of setup already exists, then using vision is probably overkill.
Without such a setup, it's important to remember that, depending on the part, designing a fixture can be costly. Also, fixtures can get tricky to design if a lot of parts will be fed to the robot without recharging the jigs. In this case, as many parts as possible must be placed within the robot's reach. Another downside arises when several different parts must be picked by the robot. In this case, several different jigs may be needed, which would therefore add to the cost. In addition, changeover time needs to be considered when estimating the efficiency of the system.
Bowl feeders are meant to take bulk items and singularize them such that one part at a time is presented to the robot, and the robot always picks this part from the same position. Once a setup like this is installed and adjusted correctly, it usually works great. There is just one pick position that needs to be programmed into the robot. However, this type of equipment can be quite expensive and hard to adjust depending on the shape of the parts being picked. Furthermore, most of the time a bowl feeder is limited to sorting only one kind of part. Switching between different parts will require adding another bowl feeding system to the robot cell.
Conveyors can be a good option for part presentation. Using a conveyor without a vision system requires a datum line or point of reference that the part will rest against such that the system will always pick that part from the same position or location. In this case, a presence sensor is probably still needed to let the robot know when the part has arrived at the picking location. Once it does, some robots have an option to pick up the part while the conveyor is moving, otherwise the conveyor must be stopped during the picking application and started it once it's done. If there isn't a datum line, or the parts are not always in the same position or at the same spot on the conveyor, a camera can locate the part on the conveyor.
A vision system is commonly used to find a part's location and orientation. There are many ways to use a vision system to accomplish part recognition. The parts can simply rest on a surface and a 2D camera will locate them. The parts can also constantly be moving on a conveyor and a fixed or robot-mounted camera can locate the parts. Or the robot can do a 3D scan of a surface and search for parts.
These options all have pros and cons. Vision is a good option if parts are frequently switched. A robot can typically be taught new parts to pick quite fast, and there are no additional hardware costs when using a vision system as opposed to jigs. Usually, the vision algorithm can simply be adjusted and the system is good to go. Different algorithms can be saved for different parts to easily be reused later.
Vision algorithms are stored on a PC versus taking up space in the plant to stock different jigs. Because the price for such a system will vary widely depending on the technology needed, it's important to get expert advice before buying. This helps avoid overkill on the specs that results in buying something expensive that won't fully be used.
For quality control applications, first identify which aspects of the part need to be examined to determine whether the part is good or bad. Then, looking at the different vision algorithms will reveal which visions systems match the system's needs and can perform the required quality inspection task. Of course, there are alternatives to using vision to perform quality checks. Following are some options and comparisons to the use of a vision system.