Two weeks ago, I attended Jason Derenick's dissertation defense, entitled - you guessed it - "A Convex Optimization Framework For Multi-Agent Motion Planning". Jason was a graduate student in the Computer and Engineering Department at Lehigh, working under the supervision of John Spletzer; he has now moved to the University of Pennsylvania, where he is a postdoctoral fellow in the GRASP (General Robotics, Automation, Sensing and Perception) laboratory.
I served as a member of his dissertation committee, which is how I got to learn about his research. It turned out I had heard about him before - he was one of the team members behind the "Little Ben" car in the 2007 DARPA Urban Challenge. That car "was one of just six driverless cars to complete the 2007 DARPA Urban Challenge and it was the only car of the six finishers whose team had not received $1 million funding from DARPA to prepare for the race," according to Lehigh's press release.
In his dissertation, Jason has exploited recent advances in convex optimization to develop real-time algorithms for motion-planning problems, with an emphasis on optimal team formation changes and optimal collaborative target tracking. These problems lead to second-order cone programming and semidefinite programming formulations, respectively.
I was very impressed by Jason's work, and the wonderful presentations he makes of it. Of course, I only got to see the product of his research in its final stages, not the six years of hard work leading to it, but it is refreshing to bypass the difficulties and hurdles of graduate work for once (I've got my own students for that) and be shown the finished product without all the setbacks. While this is true of any committee I serve on, including for students in my own department, I particularly enjoyed the application to robotics - a departure of my usual line of work and a welcome reminder of my days as an undergraduate.
One reason why people - myself included - can get so enthusiastic about the work is that Jason excels at disseminating his results and explaining them at a high level anyone can understand (it is fair to say he is the National Science Foundation's dream research assistant). In addition to designing an excellent website, he has posted on his dissertation webpage an outline of his research and several videos and simulations of his algorithms in action. My favorite is the one with the aibos taking a delta formation, because everyone likes dogs, even robot ones.