Tommy's Research Blog

In a previous blog post , I discussed my experiences reimplementing Iterative Regional Inflation by Semidefinite programming (IRIS) , which is an algorithm for finding large convex regions of collision free space (C-Free). In this blog post, I'll be discussing my experiences reimplementing shortest paths in Graphs of Convex Sets (GCS) , an algorithm for motion planning when the configuration space is described as the union of convex sets. Disclaimer: the intent is not to build a codebase for future use, since the Drake toolbox has mature implementations of both IRIS and GCS . Disclaimer: if you have not read the previous blog post , you should do so before reading this one. In this blogpost, I'll give a brief overview of the GCS paper, and then discuss my experiences reimplementing it. Before reading this blog post, you should read my previous blog post about reimplementing IRIS, as it provides some background and context which I will not repeat in this post. I have also ma

I've just begun my studying towards my PhD in computer science at MIT, and am working in the Robot Locomotion Group . This group has developed a series of projects that use formal mathematical optimization for tasks in motion planning. Because I'm interested in this area of research, I've been reading several of their papers; to better understand these papers, I've endeavored to reimplement some of their work. The two specific algorithms I'm interested in are close companions: Iterative Regional Inflation by Semidefinite programming (IRIS) can be used to find large convex regions of collision free space, and shortest paths in Graphs of Convex Sets (GCS) can be used for motion planning when the configuration space is described as the union of convex sets. I've attempted to reimplement these algorithms for simple, toy problems. Disclaimer: the intent is not to build a codebase for future use, since the Drake toolbox has mature implementations of both IRIS and