Cougar Autonomous Robotic Transport (CART)

Thanks for reading our blog! If this is your first time reading our blog, we recommend you start with our first blog post . This is bittersweet - this is most likely our last blog. If you've enjoyed it, please feel free to reach out to us either through comments or email. Final Design and Prototype The intended final design of the Cougar Autonomous Robotic Transport (CART) is shown in the figure below. The major components of the CART include its perception sensors (Intel Realsense and Evo Rangefinders), its autopilot computer (PixHawk and Nvidia Jetson), and its auxiliary sensors and controllers. Final golf cart design The team procured most of the components needed for the prototype before the campus shutdown. Unfortunately, the lab sponsoring the laser rangefinders was unable to deliver the sensors before the shutdown due to COVID-19. An overview of the final prototype is shown in the figure below. The team hopes to obtain the laser rangefinders if the campus reop...

Thanks for reading our blog! If this is your first time reading our blog, we recommend you start with our first blog post . Progress since our last post (March 20 - April 3) One of the features we’d like to implement on the golf cart is an “environmental-awareness” factor. We have a depth map of the environment that we process to create our driving commands, but we also have a RGB video feed that we can sample. Thus, we plan to feed that into an image classifier to detect people or other mobile obstacles and scale down our max speed. For example, if surrounded by a crowd of people, we’d like to reduce the max speed from 10 mph to 5 mph. We did some tests with yolov3 ("you only look once"), and results were promising, albeit slow on a CPU. Every person in every image was detected, and no objects were classified incorrectly (although our tests weren’t difficult). Tests averaged ~21 seconds to classify an image at 3.4 GHz, on the pre-trained network without adjusting anythi...