Novel research approaches demonstrate the power of working across traditional boundaries.
“I definitely want to use the ideas of hybrid systems and reachability analysis in my research, which is primarily in computational neuroscience,” Fehrman said. “I had never heard of these ideas before taking this course, but now they seem to be an obvious way to model many things that happen in the nervous system.”
Through their efforts to conquer the track, students also gained a window into just how complex it is to move self-driving vehicles out of testing scenarios into the real world.
“You always have to juggle between limitations and performance,” Gawali said. “There are n-number of approaches you can take to solve a robotics-learned problem, but there is no one true correct solution that will work for all situations.”
In the second round of competition, Bezzo introduced a surprise obstacle that would block the path. This time, the prize would go to the team that traveled the nearly seven-meter track in the fastest time. Team One grabbed top spot by finishing in 22 seconds.
“This was an impressive performance because the robot was driving at an accelerated and sustained speed in the straight segments, slowing down during the turns to align itself for the next straight segment, and then also maneuvering to avoid the obstacle,” Bezzo said.