Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization

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 r...

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Bibliographic Details
Published in:2020 European Control Conference (ECC) pp. 1871 - 1878
Main Authors: Lew, Thomas, Bonalli, Riccardo, Pavone, Marco
Format: Conference Proceeding
Language:English
Published: EUCA 01.05.2020
Subjects:
Collision avoidance
Heuristic algorithms
Planning
Programming
Trajectory optimization
Uncertainty
Online Access:Get full text
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Summary: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.
DOI:10.23919/ECC51009.2020.9143595