GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming

Riccardo Bonalli (1) , Abhishek Cauligi (1) , Andrew Bylard (1) , Marco Pavone (1)

1 AA Stanford - Department of Aeronautics and Astronautics [Stanford]

Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. However, most available methods lack rigorous performance guarantees and they are often tailored to specific optimal control setups. In this paper, we present GuSTO (Guaranteed Sequential Trajectory Optimization), an algorithmic framework to solve trajectory optimization problems for control-affine systems with drift. GuSTO generalizes earlier SCP-based methods for trajectory optimization (by addressing, for example, goal-set constraints and problems with either fixed or free final time) and enjoys theoretical convergence guarantees in terms of convergence to, at least, a stationary point. The theoretical analysis is further leveraged to devise an accelerated implementation of GuSTO, which originally infuses ideas from indirect optimal control into an SCP context. Numerical experiments on a variety of trajectory optimization setups show that GuSTO generally outperforms current stateof-the-art approaches in terms of success rates, solution quality, and computation times.

Domaines: Optimisation et contrôle [math.OC]

Riccardo Bonalli, Abhishek Cauligi, Andrew Bylard, Marco Pavone. GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming. 2019 International Conference on Robotics and Automation (ICRA), May 2019, Montreal, Canada. pp.6741-6747, ⟨10.1109/ICRA.2019.8794205⟩. ⟨hal-03467734⟩