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 . 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...

Thanks for reading our blog! If this is your first time seeing on our blog, we recommend you start at our first blog post . Progress since our last post (March 6 - March 20) According to our last blog post, we planned to mount all of our sensors onto the CART and have them operational. However, there have been numerous setbacks. The first (planned) setback was spring break. After a grueling week of midterms and project, we took a break and spent some time recuperating. As a result, productivity was low from 3/9 to 3/13. In addition to taking a break, we had other responsibilities ranging from job interviews to thesis projects. However, we planned to increase the amount of time dedicated to the CART (starting March 16th). Unfortunately, as you are no doubt aware, the COVID-19 outbreak was declared a pandemic, and the situation evolved rapidly. Over spring break, the university went from one extreme to the other - at first, we thought no classes would get cancelled. Now, all of o...

Thanks for reading our blog! If this is your first time seeing on our blog, we recommend you start at our first blog post . Progress since our last post (February 14 - March 6) We finally installed our odometry solution. We had to mount it externally, since we were not able to mount it on an axle within the motor that drives the CART (this is the typical implementation, and is what almost every car does). This is because we are limited to solutions that do not involve permanent modifications to the CART. To implement our odometry solution, we used 24 equally-spaced magnets (about every 2 inches or so) around the perimeter of the wheel. This provided a high angular resolution. We validated the odometer by comparing the reported speed to the estimated speeds provided by two different GPS-based speedometer apps on an iPhone 7. Odometry: Hall effect sensor mounted to the CART It has detected a magnet, so the LED is red After validating the odometer, we designed and 3D-printed ...

Thanks for reading our blog! If this is your first time seeing on our blog, you might want to start by reading our first blog post . Progress since our last post Since the last post, we’ve fully pivoted away from using 3D LIDAR. Feel free to read our last blog post for more information, but the key point is that we don’t have enough money to buy a 3D LIDAR (but we hope future teams will be able to purchase and use the Velabit when it is released later this year). Instead, we’ll utilize depth cameras and high precision, 1-D laser scanners. We have a free depth camera (an Intel RealSense , courtesy of IEEE Makerspace) and a couple of loaner laser scanners (courtesy of the NCALM ). We were able to test the RealSense earlier this week. After reconfiguring the camera for a couple of hours, we were able to get indoor depth readings for distances of up to 23 feet, (~7m) which is great, but below our goal of 10m. On a bright but cloudy day, we were able to get depth readings of up to 40 f...

Thanks for reading our blog! If this is your first time seeing on our blog, you might want to read our first blog post . Progress since our last post It has been a while since we last posted. Since our last post, we’ve ordered and received many of our components. We had planned to work on the CART over the break, but due to delays in the procurement process, many of our parts didn’t arrive until the end of the break. Unfortunately, this meant that we were unable to get much work done during the winter break. We’ve hit the ground running this semester. So far, we’ve installed an upgrade to the braking system to allow an additional (more rapid) braking mode to be activated in the event of an emergency. This is a necessary safety feature before we can begin any autonomous driving tests as the pre-existing braking was insufficient for emergency situations. We have also replaced the pre-existing remote controller with a standard COTS 3-channel transmitter/receiver and tuned the motor ...

Thanks for reading our blog! If you haven’t seen it already, you may want to check out our first blog post . Introduction and Problem As discussed in our previous post, our challenge lies in designing an effective ‘short-distance’ transportation system for visitors and students/faculty unable to walk around the University of Houston campus. A potential solution lies in using golf carts to navigate between points of interest on campus. Golf carts are ideal because they are small enough to drive on sidewalks and paths that are inaccessible to regular vehicles. Further, by making these golf carts autonomous, there is no need for a dedicated driver, so the University can reduce the operating costs of this transportation system. Eliminating the human driver reduces cost while also increasing the safety of cart, as more than 90% of crashes are caused by human error ( NSC 2019 ). Unfortunately, there are no commercially available autonomous golf carts. Despite multiple attempts (e.g. Aur...