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Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty

Nakka, Yashwanth Kumar and Chung, Soon-Jo (2022) Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty. IEEE Transactions on Robotics . ISSN 1552-3098. doi:10.1109/tro.2022.3197072. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20220908-230525012

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Abstract

In this article, we present generalized polynomial chaos-based sequential convex programming (gPC-SCP) to compute a suboptimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control (SNOC) problem. The approach enables motion planning for robotic systems under uncertainty. The gPC-SCP method involves two steps. The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming for trajectory generation and control. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC. We also present the predictor–corrector extension (gPC-SCP_(PC)) for real-time motion trajectory generation in the presence of stochastic uncertainty. In the gPC-SCP_(PC) method, we first predict the uncertainty using the gPC method and then optimize the motion plan to accommodate the uncertainty. We empirically demonstrate the efficacy of the gPC-SCP and the gPC-SCP_(PC) methods for the following two test cases: first, collision checking under uncertainty in actuation and physical parameters and second, collision checking with stochastic obstacle model for 3DOF and 6DOF robotic systems. We validate the effectiveness of the gPC-SCP method on the 3DOF robotic spacecraft testbed.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/TRO.2022.3197072DOIArticle
ORCID:
AuthorORCID
Nakka, Yashwanth Kumar0000-0001-7897-3644
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:This work was supported by the Jet Propulsion Laboratory. The authors would like to thank A. Rahmani, F. Y. Hadaegh, J. Burdick, R. Murray, and Y. Yue for stimulating discussions and technical help.
Group:GALCIT
Funders:
Funding AgencyGrant Number
JPLUNSPECIFIED
DOI:10.1109/tro.2022.3197072
Record Number:CaltechAUTHORS:20220908-230525012
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220908-230525012
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116694
Collection:CaltechAUTHORS
Deposited By: Melissa Ray
Deposited On:08 Sep 2022 23:10
Last Modified:08 Sep 2022 23:10

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