(Georgia Institute of Technology, 2015-05)
Kobilarov, Marin; Ta, Duy-Nguyen; Dellaert, Frank
This paper studies an optimization-based approach for solving optimal estimation and optimal control problems through a unified computational formulation. The goal is to perform trajectory estimation over extended past horizons and model-predictive control over future horizons by enforcing the same dynamics, control, and sensing constraints in both problems, and thus solving both problems with identical computational tools. Through such systematic estimation-control formulation we aim to improve the performance of autonomous systems such as agile robotic vehicles. This work focuses on sequential sweep trajectory optimization methods, and more specifically extends the method known as differential dynamic programming to the parameter-dependent setting in order to enable the solutions to general estimation and control problems.
(Georgia Institute of Technology, 2014-05)
Ta, Duy-Nguyen; Kobilarov, Marin; Dellaert, Frank
n this paper, we present a factor graph framework to solve both estimation and deterministic optimal control problems, and apply it to an obstacle avoidance task on Unmanned Aerial Vehicles (UAVs). We show that factor graphs allow us to consistently use the same optimization method, system dynamics, uncertainty models and other internal and external
parameters, which potentially improves the UAV performance
as a whole. To this end, we extended the modeling capabilities of factor graphs to represent nonlinear dynamics using constraint factors. For inference, we reformulate Sequential Quadratic
Programming as an optimization algorithm on a factor graph
with nonlinear constraints. We demonstrate our framework on
a simulated quadrotor in an obstacle avoidance application.