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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Veröffentlicht in:2020 European Control Conference (ECC) S. 1871 - 1878
Hauptverfasser: Lew, Thomas, Bonalli, Riccardo, Pavone, Marco
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: EUCA 01.05.2020
Schlagworte:
Collision avoidance
Heuristic algorithms
Planning
Programming
Trajectory optimization
Uncertainty
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Zusammenfassung: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