Robot navigation using convex model predictive control and approximate operating region optimization

Abstract

A method for real-time robot navigation with obstacle avoidance is presented. A two-stage approach is proposed: the use of online Simulated Annealing (SA) to optimize a convex operating region in configuration space is paired with Model Predictive Control (MPC) to determine a locally optimal motion plan. The method retains recursive feasibility guarantees from MPC and speed of solution of the convex optimal control problem. Meanwhile, the convexification performed by the SA enables the method to operate with unstructured environment representations, such as point clouds or line scans.