All stars are not created equal. When you look out into the night sky, you are seeing all sorts of unique and interesting objects. Some stars are small and cool (at least, compared to our Sun), and live for many billions of years. Others have evolved and inflated to enormous sizes- even over 1,000 times the size of our sun. There is a class of bright, blue stars called “Classical Be stars” that are between about 5 – 20 times more massive than the sun, and spin so quickly that they are nearly torn apart by the resulting centrifugal force. These stars also have disks that grow and shrink, appear and disappear. Classical Be stars are unique in astronomy, because their disks originate from the stars themselves. Material from the surface of the star is flung outward with so much speed (and angular momentum) that it is launched into orbit, and then settles into a disk in an event called an “outburst”. Lehigh physics professor Joshua Pepper and graduate student Jonathan Labadie-Bartz are studying these objects because there is still much that is unknown, especially regarding the physical mechanisms behind outbursts. The header image shows an artist’s rendition of a Be star and its disk.
John Spletzer is an Associate Professor of Computer Science and Engineering at Lehigh University. Below he details the
The inspiration for this project came during my sabbatical at Love Park Robotics, LLC (LPR) in 2015. LPR is a robotics startup doing work in industrial perception, and the primary project I worked on was a vision-based pallet detection system for use by Automated Guided Vehicles (AGVs). AGVs are autonomous vehicles operating in warehouse environments. Think “robot forklift,” and you have the right idea. To estimate their position and orientation, AGVs typically rely upon 2D LIDAR (laser scanner) based localization systems that track reflector targets surveyed into the warehouse. The approach is very effective, and can provide sub-centimeter levels of accuracy. However, the process of installing the targets is both time consuming and expensive. Furthermore, it needs to be repeated any time the warehouse is reconfigured. Conversations with Tom Panzarella, CEO of LPR, lead us to investigate an alternative approach. Our hypothesis was that recent advances in 3D LIDAR systems would allow us to estimate AGV pose by tracking natural features already existing in the warehouse. This would eliminate the need for retroreflector targets all together. We refer to this technology as AGV-3D. From my NSF CAREER research, my lab had already demonstrated that a smart wheelchair system using a similar approach could reliably navigate in an urban environment without GPS. You can see an early video from the project here: