Understanding TrajOpt

TrajOpt $[1]$ is an optimization based approach for motion planning. More specifically, it uses a sequential convex optimization procedure along with a formulation of collision constraints to find locally optimal planning trajectories, even for robotic systems that have a large number of degrees of freedom. This post will go into the details of the optimization part, and give an outline of how it can be implemented. My implementation can be found here [Read More]

Multi Vehicle Mixed Integer Programming

In this post, we take a look at this $[1]$ paper, which introduces a simple, yet interesting approach to solving a multi-vehicle path planning problem. My implementation of the algorithm that was used to evaluate its performance and generate all the results in this post can be found here $\newcommand{\ith}{i^{th}}$ $\newcommand{\pth}{p^{th}}$ $\newcommand{\qth}{q^{th}}$ $\newcommand{\lth}{l^{th}}$ $\newcommand{\MR}{\mathbb{R}}$ $\newcommand{\xpi}{x_{pi}}$ $\newcommand{\ypi}{y_{pi}}$ $\newcommand{\xqi}{x_{qi}}$ $\newcommand{\yqi}{y_{qi}}$ $\newcommand{\xmin}{x_{min}}$ $\newcommand{\xmax}{x_{max}}$ $\newcommand{\ymin}{y_{min}}$ $\newcommand{\ymax}{y_{max}}$ $\newcommand{\xlmin}{x_{l,min}}$ $\newcommand{\xlmax}{x_{l,max}}$ $\newcommand{\ylmin}{y_{l,min}}$ $\newcommand{\ylmax}{y_{l,max}}$ $\newcommand{\xlimin}{x_{li,min}}$ $\newcommand{\xlimax}{x_{li,max}}$ $\newcommand{\ylimin}{y_{li,min}}$ $\newcommand{\ylimax}{y_{li,max}}$ $\newcommand{\cplx}{c_{pl,x}}$ $\newcommand{\cply}{c_{pl,y}}$ $\newcommand{\dpqx}{d_{pq,x}}$ $\newcommand{\dpqy}{d_{pq,y}}$ $\newcommand{\tpli}{t_{pli}}$ $\newcommand{\spi}{s_{pi}}$ $\newcommand{\spij}{s_{pij}}$ $\newcommand{\spinext}{s_{p,i+1}}$ $\newcommand{\spn}{s_{pN}}$ $\newcommand{\spf}{s_{pf}}$ $\newcommand{\wpi}{w_{pi}}$ $\newcommand{\wpij}{w_{pij}}$ $\newcommand{\wpn}{w_{pN}}$ $\newcommand{\upi}{u_{pi}}$ $\newcommand{\upik}{u_{pik}}$ $\newcommand{\vpi}{v_{pi}}$ $\newcommand{\vpik}{v_{pik}}$ $\newcommand{\Ap}{A_{p}}$ $\newcommand{\Bp}{B_{p}}$ $\newcommand{\qp}{q_{p}}$ $\newcommand{\rp}{r_{p}}$ $\newcommand{\pp}{p_{p}}$ [Read More]