Vision System and its application,Problems
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Robotic vision systems allow robots to identify objects, detect people, check quality, and gather information about their surroundings. A typical vision system includes a camera to capture images, a processor to analyze the images, and cabling to transmit the image data. Vision systems enable robots to perform tasks like facial recognition, ensure safety, locate parts, and perform quality control inspections. While lighting, deformation, backgrounds, scale, and movement present challenges, vision systems continue to advance with improvements in areas like insect-inspired design and affordable all-in-one cameras.
Vision System and its application,Problems
- 2. Robotics Vision System Presented by- Nikhil Chavda(U14ME075)
- 4. Robotic Vision Robotic vision is similar to human vision – it provides valuable information that the robot can use to interact with the world around it. Robots equipped with vision can identify colors, find parts, detect people, check quality, information about its surroundings, read text.
- 5. Vision System Components Camera This is the part of the system that will take light from the outside world and convert it into digital data that can be processed and analyzed by the system. Today, the cameras used in robotic vision range from 2 megapixels on up with full color and 4,095 different shades to work with. This large amount of data has made image processing easier, as it provides a wealth of information, but not necessarily faster. Processor The processor converts all the data from the camera into something useful to the robot.
- 6. Continue.. Cabling In earlier technology, the communication cables used for vision systems were clunky and limited in how far they could send the data without loss. Around 2009, Adimec developed a new way of sending data that allowed over 6 Gbps of data transmission over coaxial cable, and named it ‘CoaXPress’
- 7. Vision System Applications Facial recognition Facial recognition is the ability of robotic systems to match an image of a person to data stored in its memory. For example, programmable Aldebaran’s NAO robot recognize face and then respond with a message using name, creating a personalized experience when interacting with the robot.
- 8. Continue… Safety systems Instead of risking the lives of people, we can use a robot to deny entry or search for unauthorized persons based on a database of approved facial scans. Robotic vision has helped to free the robot from the traditional safety cage that is common in industrial applications. The Baxter robot created by Rethink Robotics is a perfect example of this, it has 360 degree facing camera. Anytime Baxter senses a person, the robot slows down to a safe speed and closely monitors system feedback for any indication of a collision, stopping all movement before anyone can get hurt. On top of this, Baxter uses its vision system to find parts and adjust positioning as needed.
- 9. Continue… Part finding Vision systems use processor to pick out specific parts from a complex image. Once the system finds a part, it uses the data gathered from the visual information to modify its program and complete tasks as directed. To use a vision system this way, there must be some form of calibration where the robot can relate the visual data to distance. With 2D vision, or a single camera, the camera needs to be in the same position each time it takes a picture, and there must be some form of calibration to find distance from this point. With 3D vision or two cameras, or images from two locations, determine the distance.
- 10. Continue… Quality control The 3D system will require calibration as well, and in the instance of two cameras, the location of the cameras in respect to one other is part of the calibration. This same type of system can measure part features to the micron level, insuring the quality of each part during operation.
- 11. Challenges: Lighting Deformation Background Scale Movement
- 12. What’s Trending..?? CMUcam5 Pixy is an all in one vision system that works with Arduino, Raspberry Pi and BeagleBone for color and object recognition, with facial recognition on the way. Previously, it took either a large amount of work or a costly system to provide this functionality for hobby robots, but Pixy has made it easy. Insect vision is the hot topic for robotic vision. Insects react to the world around them and chase prey using simplistic eyes and very little brainpower, which is something scientists are hoping to copy for vision systems. The idea is to use multiple vision cells and change the information collected for ease of processing.
- 13. Conclusion Vision systems have become a common feature of many robots and there is no end in sight to the possibilities these systems create. As this technology continues to evolve, robots will have access to new and exciting ways to interact with the world around them.