A new optimization framework for robot motion planning

MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles. It isn't easy for a robot to find its way out of a maze. Picture the machines trying to traverse a kid's playroom to reach the kitchen, with miscellaneous toys scattered across the floor and furniture blocking some potential paths. This messy labyrinth requires the robot to calculate the most optimal journey to its destination, without crashing into any obstacles. What is the bot to do? MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers' "Graphs of Convex Sets (GCS) Trajectory Optimization" algorithm presents a scalable, collision-free motion planning system for these robotic navigational needs. The approach marries graph search (a method for finding discrete paths in a network) and convex optimization (an efficient method for optimizing continuous variables so that a given cost is minimized), and can quickly find paths through maze-like environments while simultaneously optimizing the trajectory of the robot. GCS can map out collision-free trajectories in as many as 14 dimensions (and potentially more), with the aim of improving how machines work in tandem in warehouses, libraries, and households.