Convex Trajectory Planning [About This Issue]

This IEEE Control Systems issue includes one feature and application for control. The “Convex Optimization Trajectory Generation,” by Danylo Malyuta, Taylor P. Reynolds, Michael Szmuk, Thomas Lew, Riccardo Bonalli, Marco Pavone, Behçet Açikmes¸e, provides a tutorial on the optimization methods that have proven themselves in both theory practice to be fast reliable at producing dynamically feasible trajectories nonlinear systems subject nonconvex constraints. authors their colleagues are original developers of discussed: lossless convexification two sequential convex programming algorithms called xmlns:xlink="http://www.w3.org/1999/xlink">SCvx xmlns:xlink="http://www.w3.org/1999/xlink">GuSTO . At core, all three leverage reliable, low-level optimizer solves control problems through problem reformulation, linearization, or both. Over past decade, these generated growing interest across academia industry, including from organizations such as NASA, Masten Space Systems, SpaceX, Blue Origin. article reader with an intimate understanding what it takes use—or even extend—each generate nontrivial trajectories. Open source code written Julia language accompanies can serve ready-to-use toolbox reader’s further work programming.