Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

Thomas Lew (1) , Riccardo Bonalli (1) , Marco Pavone (1)

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

Planning safe trajectories for nonlinear dynamical systems subject to model uncertainty and disturbances is challenging. In this work, we present a novel approach to tackle chance-constrained trajectory planning problems with nonconvex constraints, whereby obstacle avoidance chance constraints are reformulated using the signed distance function. We propose a novel sequential convex programming algorithm and prove that under a discrete time problem formulation, it is guaranteed to converge to a solution satisfying first-order optimality conditions. We demonstrate the approach on an uncertain 6 degrees of freedom spacecraft system and show that the solutions satisfy a given set of chance constraints.

Domaines: Optimisation et contrôle [math.OC]

Thomas Lew, Riccardo Bonalli, Marco Pavone. Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization. 2020 European Control Conference (ECC), May 2020, Saint Petersburg, Russia. pp.1871-1878, ⟨10.23919/ECC51009.2020.9143595⟩. ⟨hal-03467576⟩