Optimizing Trajectories with Closed-Loop Dynamic SQP
Name
2109.07081v2.pdf
Description
Accepted version
Size
1.35 MB
Format
Adobe PDF
Checksum (MD5)
72b176752a87bf2afa6dd5673059fb93
Author(s)
Singh, Sumeet
Slotine, Jean-Jacques
Sindhwani, Vikas
Date Issued
May 23, 2022
Publisher
IEEE
Citation
S. Singh, J. -J. Slotine and V. Sindhwani, "Optimizing Trajectories with Closed-Loop Dynamic SQP," 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 2022, pp. 5249-5254.
Version
Author's final manuscript
Abstract
Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e.g., control bounds, obstacle avoidance). However, a naïve implementation of NLP algorithms, e.g., shooting-based sequential quadratic programming (SQP), may suffer from slow convergence -- caused from natural instabilities of the underlying system manifesting as poor numerical stability within the optimization. Re-interpreting the DDP closed-loop rollout policy as a sensitivity-based correction to a second-order search direction, we demonstrate how to compute analogous closed-loop policies (i.e., feedback gains) for constrained problems. Our key theoretical result introduces a novel dynamic programming-based constraint-set recursion that augments the canonical "cost-to-go" backward pass. On the algorithmic front, we develop a hybrid-SQP algorithm incorporating DDP-style closed-loop rollouts, enabled via efficient parallelized computation of the feedback gains. Finally, we validate our theoretical and algorithmic contributions on a set of increasingly challenging benchmarks, demonstrating significant improvements in convergence speed over standard open-loop SQP.
Description
2022 International Conference on Robotics and Automation (ICRA), 23-27 May, Philadelphia, PA, USA
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-ShareAlike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra46639.2022.9811562
