Constrained Sampling-based Trajectory Optimization using Stochastic Approximation
Optimization and Control
2019-11-13 v1
Abstract
We propose a sampling-based trajectory optimization methodology for constrained problems. We extend recent works on stochastic search to deal with box control constraints,as well as nonlinear state constraints for discrete dynamical systems. Regarding the former, our strategy is to optimize over truncated parameterized distributions on control inputs. Furthermore, we show how non-smooth penalty functions can be incorporated into our framework to handle state constraints. Simulations on cartpole and quadcopter show that our approach outperforms previous methods on constrained sampling-based optimization, in terms of quality of solutions and convergence speed.
Cite
@article{arxiv.1911.04621,
title = {Constrained Sampling-based Trajectory Optimization using Stochastic Approximation},
author = {George I. Boutselis and Ziyi Wang and Evangelos A. Theodorou},
journal= {arXiv preprint arXiv:1911.04621},
year = {2019}
}